Here are the top 30 AIOps interview questions with answers:

1. What is AIOps?

AIOps stands for Artificial Intelligence for IT Operations. It is a set of technologies and practices that use machine learning and artificial intelligence to automate IT operations tasks, such as monitoring, alerting, and troubleshooting.

2. What are the benefits of AIOps?

The benefits of AIOps include:

Increased efficiency: AIOps can automate many of the manual tasks involved in IT operations, freeing up IT staff to focus on more strategic work.
Improved reliability: AIOps can identify and resolve problems more quickly, before they impact users.
Reduced costs: AIOps can help to reduce the cost of IT operations by automating tasks and by preventing problems from occurring.

3. What are the challenges of AIOps?

The challenges of AIOps include:

Data: AIOps requires a lot of data to train and operate effectively. This data can be difficult and expensive to collect and prepare.
Complexity: AIOps is a complex technology that requires a deep understanding of machine learning and artificial intelligence.
Adoption: AIOps is still a relatively new technology, and there is a lack of understanding and adoption among IT teams.

4. What are the different types of AIOps solutions?

There are two main types of AIOps solutions:

Domain-centric AIOps: These solutions are focused on a specific domain, such as networking, storage, or security.
Domain-agnostic AIOps: These solutions are designed to work across multiple domains.

5. What are the most popular AIOps tools?

The most popular AIOps tools include:

New Relic AIOps: New Relic AIOps is a domain-agnostic AIOps solution that uses machine learning to automate IT operations tasks.
AppDynamics AIOps: AppDynamics AIOps is a domain-centric AIOps solution that focuses on application performance.
IBM Cloud Pak for Watson AIOps: IBM Cloud Pak for Watson AIOps is a domain-agnostic AIOps solution that uses Watson AI to automate IT operations tasks.
Splunk Cloud AIOps: Splunk Cloud AIOps is a domain-agnostic AIOps solution that uses Splunk data to automate IT operations tasks.
Datadog AIOps: Datadog AIOps is a domain-agnostic AIOps solution that uses Datadog data to automate IT operations tasks.

6. What are the different stages of AIOps maturity?

There are four main stages of AIOps maturity:

Reactive: In the reactive stage, IT teams are manually monitoring their systems and responding to incidents as they occur.
Proactive: In the proactive stage, IT teams are using machine learning to identify potential problems before they occur.
Predictive: In the predictive stage, IT teams are using machine learning to predict future problems and take preventive action.
Self-healing: In the self-healing stage, IT systems are able to automatically detect and resolve problems without any human intervention.

7. What are the different use cases for AIOps?

AIOps can be used for a variety of use cases, including:

Monitoring: AIOps can be used to monitor IT systems and identify potential problems.
Alerting: AIOps can be used to automatically generate alerts when problems are detected.
Troubleshooting: AIOps can be used to help IT teams troubleshoot problems.
Root cause analysis: AIOps can be used to identify the root cause of problems.
Predictive maintenance: AIOps can be used to predict when problems are likely to occur and take preventive action.
Self-healing: AIOps can be used to automate the process of detecting and resolving problems.

8. What are the skills required for an AIOps engineer?

The skills required for an AIOps engineer include:

IT operations experience: AIOps engineers need to have a deep understanding of IT operations principles and practices.
Machine learning skills: AIOps engineers need to have strong skills in machine learning and artificial intelligence.
Data analysis skills: AIOps engineers need to be able to analyze large amounts of data to identify problems and trends.
Communication skills: AIOps engineers need to be able to communicate effectively with IT teams and stakeholders.
Problem-solving skills: AIOps engineers need to be able to identify and solve problems quickly and efficiently.

9. What are the career opportunities for AIOps engineers?

The career opportunities for AIOps engineers are growing rapidly as more and more organizations adopt AIOps solutions. AIOps engineers can find jobs in a variety of industries, including:

IT: AIOps engineers can work for IT organizations of all sizes, from small businesses to large enterprises.

10. How can AIOps improve capacity planning?

AIOps analyzes historical and real-time data to predict resource needs, enabling efficient capacity planning and resource allocation.

11. Explain the concept of “proactive monitoring” in AIOps.

Proactive monitoring involves using AI and ML to predict potential issues before they impact the system, allowing IT teams to take preventive measures.

12. What is “self-healing” in the context of AIOps?

Self-healing refers to AIOps systems automatically detecting and addressing issues without human intervention, leading to quicker problem resolution.

13. How does AIOps contribute to business agility?

AIOps provides real-time insights into IT operations, enabling businesses to respond quickly to changing demands and ensure optimal performance.

14. What is the importance of real-time analytics in AIOps?

Real-time analytics enable IT teams to quickly identify and address issues as they occur, minimizing downtime and maintaining service availability.

15. How does AIOps handle data from various sources and formats?

AIOps platforms collect and normalize data from diverse sources, making it easier to analyze and correlate information across the IT environment.

16. What is the role of “algorithmic noise reduction” in AIOps?

Algorithmic noise reduction involves filtering out irrelevant data to improve the accuracy of AI-driven insights and predictions.

17. Explain the concept of “digital experience monitoring” in AIOps.

Digital experience monitoring involves tracking user interactions and feedback to assess the quality of services provided by IT systems.

18. How does AIOps support “automated incident response”?

AIOps can trigger predefined responses to common incidents based on historical patterns, reducing manual intervention and minimizing downtime.

19. Describe the “unsupervised learning” approach in AIOps.

Unsupervised learning involves training AI models without labeled data, allowing them to discover patterns and anomalies independently.

20. What are the challenges of implementing AIOps in an organization?

Challenges may include data integration, model accuracy, change management, and ensuring alignment with business goals.

21. Explain the Hidden Markov Model.

The Hidden Markov model is a probabilistic model which is used to identify the probabilistic character of any event. It says that an observed event is related to a set of probability distributions. If a system is being modeled into a Markov’s chain, then the main goal of HMM is to identify the hidden layers of the Markov’s chain. Hidden means that the particular state is not observable to the observer. It is generally used for temporal data. HMM finds its application in reinforcement learning, temporal pattern recognition, etc.

22. What do you understand by hyperparameters?

Hyperparameters are the parameters that control the entire training process. These variables are adjustable and have a direct impact on how successfully a model trains. They are declared beforehand. Model hyperparameters, which cannot be inferred while fitting the machine to the training set because they refer to the model selection task, and algorithm hyperparameters, which have no effect on the model’s performance but affect the speed and quality of the learning process, are two types of hyperparameters.

The selection of good hyperparameters is crucial for the training process. Activation function, alpha learning rate, hidden layers, number of epochs, number of branches in a decision tree, etc. are some of the examples of hyperparameters.

23. What is Overfitting?

Overfitting is a concept in data science when a data point does not fit against its training model. When the raining model is fed with data, there is a possibility that it might encounter some noise that cannot fit into the statistical model. This happens when the algorithm cannot perform accurately against unseen data.

24. What are the techniques used to avoid overfitting?

If we can detect overfitting at an early stage, it will be very useful for our training model. There are several methods up our sleeves that can be used to avoid overfitting-

Cross-validation: Cross-validation is a resampling technique for evaluating machine learning models on a small sample of data.
Remove features: We can remove the unnecessary features of the models to encompass the outliers.
Early stopping: Early stopping is a type of regularization used in machine learning to minimize overfitting when using an iterative method like gradient descent to train a learner. Early stopping criteria specify how many iterations can be completed before the learner becomes over-fit.
Training with more data: We can train our model with more data to accommodate outliers.
Regularization: In machine learning, regularization is a method to solve the over-fitting problem by adding a penalty term with the cost function.
Ensembling: Ensemble learning refers to combining the predictions from two or more models.

25. What is Natural Language Processing?

Natural Language Processing (NLP) is a field of Artificial Intelligence, concerned with giving computers the ability to understand and interact in human languages in a way humans can.

NLP combines rule-based modeling of human language with statistical, machine learning, and deep learning models. This makes a computer fully understand and comprehend human language in the form of voice or text. Voice-operated GPS systems, speech-to-text systems, customer service chat boxes, etc. use NLP.

26. What is the difference between eigenvalues and eigenvectors?

Eigenvalues are the coefficients given to eigenvectors that determine the length or magnitude of the vectors. Eigenvalues are unit vectors having magnitude 1. A negative eigenvalue, for example, may scale the eigenvector in the opposite way.
Eigenvectors are unit vectors, meaning their length or magnitude is the same as 1.0. They’re also known as right vectors, which simply means “column vectors” (as opposed to a row vector or a left vector). A right-vector is a vector in the traditional sense.

27. What are the different components of an expert system?

An expert system is a computer program that simulates the judgement and behavior of a human or an organization with expert knowledge and expertise in a particular field using artificial intelligence (AI) technologies.

The expert systems belong to an important domain of Artificial Intelligence, which is used to solve complex problems using extraordinary human intelligence and expertise.

The different components used to build an expert system are:

Knowledge base- It is a storage area that contains domain-specific, high-quality knowledge.
Inference engine- The Inference engine uses and manipulates the knowledge from the knowledge base.
User Interface- It provides interaction between the expert system and the user.

28. What are some differences between classification and regression?

Regression and classification are both supervised learning algorithms. Both work on labeled data and are used to predict in machine learning. The difference, however, arises from the manner in which they are used.

29. What is an Artificial Neural Network? What are some commonly used Artificial Neural networks?

Artificial Neural networks, simply called Neural networks, are computer systems based on units called nodes or artificial neurons, which resemble the neurons in human brains. Each node can transmit a signal from one node to another.

30. What Is Game Theory?

Game theory is a branch of AI that attempts to define a strategic game with predefined rules and outcomes between two players of equal rationality. Every player is selfish and tries to maximize the reward to be obtained using a particular strategy. All the players abide by certain rules in order to receive a playoff- which is a reward. Therefore, a game can be defined as a set of players, actions, strategies, and a final reward.

Game theory and AI are related to each other and complement each other. Game theory is used in AI situations where multiple agents are in an environment trying to achieve a goal. Various games are logical and have a set of pre-decided rules like chess, poker, etc., which can be made available digitally with the help of Artificial Intelligence and Game Theory.

As IT is a vast and growing sector, it always demands skillful professionals with some certifications on their related tools or working courses that give a great advantage to both organizations and professionals. A Certification can give you more skills that can boost your resume and career as well that can offer you more lucrative salaries and opportunities. Certifications have always been playing a key role to increase the value of your skills and getting an advantage in getting a job in your particular desired domain.

Some IT certifications.

Master in DevOps engineering – As DevOps has beaten all the traditional SDLC processes, it has become a more demanding and favorite process.

It has been seen DevOps is growing market day by day and it will be continued in upcoming years as well. This master certification is really important as it offers to learn all the open-source major toolsets which can boost your knowledge as well as career.

So do check this certification as it is still in demand and is continuously growing and offers a good salary package.

Site reliability engineering (SRE) – SRE is founded byGoogle Company and also works the same as DevOps, but the only difference is the main focus of SRE is to enhance the reliability and better performance of applications, with automation and continuous integration and delivery.

SRE has also earned its value in the market and come in demand. It has also toolsets like DevOps to automate their work. You can learn everything during the certification of SRE. As per the 2021 report, 22% of organizations have adopted SRE, and it is expecting more growth in 2022.

DevSecOps – The main focus of DevSecOps is security but it stands for all development, security, and operations.

It believes security is everyone’s priority. Everyone should work by keeping security in mind, so the security level won’t compromise.

DevSecOps certifications are essential as the security of software is very important.

This certification will teach you the way to keep the security in all the parts of work and how security matters when it comes to deployment in the market.

It takes the responsibility to save the software from all the loopholes and people like hackers as much as possible. It also has its toolsets to automate the work and to remove manual errors. It is also achieving very nice growth and it will be continued in 2022 as well.

Docker and Kubernetes – Docker and kubKubernetesth are essential to each other as Docker is a container and Kubernetes is the management tool of those containers. That’s why both certificates are necessary if you thinking to do one. Some company uses Docker Swarm instead of Kubernetes. Both works are the same but the thing is most of the companies prefer Kubernetes, so go with this. Kubernetes is vaster than Docker Swarm. As both had images in the market it is certain both the tools will keep continuing their growth in the market and also both are important to the DevOps process, so yes it comes under important IT certification of 2022.

Artificial Intelligence for IT Operations (AIOPS) –

AIOPS is the application of AI, machine learning, natural language processing, etc to traditional IT Ops activities and tasks. AIOps combine big data and machine learning to automate IT operations processes, which comprises event correlation, anomaly detection, and causality determination.

It is hard to manage today’s dynamic and constantly changing IT environments with yesterday’s tools. Microservices and containerization make it necessary to modernize. The evolution of IT infrastructure is a must. So the certification of AIOPS is also important because AIOPS is in trend and we can see this in 2022 as well.

Gitops –

GitOps is a method to manage infrastructure and application configurations using Git.  GitOps is built around the developer experience and help teams in managing infrastructure using the same tools and processes they use for software development.

Gitops certification enables you to leverage so many benefits like:-

Enhance developer productivity

An enhanced version experience for DevOps teams

Reduce failed deployments

Recover from outages quickly

Mlops –

MLOps certification is a set of practices that intends to deploy and maintain machine learning models in production.

MLOps allow the continuous delivery of high-quality ML applications into production at scale.

MLOps is the communication between data scientists and the operations team. It is deeply collaborative and designed to eliminate waste, automate as much as possible, and produce more richer and consistent insights through machine learning. Machine learning is a game-changer for business as its environment is changing from manual to automation.

MLOps have all the capabilities that are required from data science, product teams, and IT to deploy, operate, govern, and secure models in production. It comprises the following components that enable an automated ML pipeline as well that maximizes your ML performance and ROI:                                                                                                                                                          

Model serving and pipelining

Model service catalog(s) for all models in production

Model version control

Infrastructure management

Monitoring

Security

Governance

Connections with all data sources and best-in-class tools for model training, development, infrastructure, and compliance.

Artificial intelligence (AI) –

Artificial intelligence is a program that is designed to work like human brains such as thinking, learning, making decisions on their own.

Artificial intelligence has been introduced in 1956 at a conference by John McCarthy and this is the first person who thought about artificial intelligence. That’s why we call him the father of AI.

As per the report, it will be approx 58 million job roles in the AI sector in 2022. So AI is present as well as future because we are already using AI technologies like Alexa, Siri on iPhone, etc.

DataOps –

DataOps is designed to solve problems organizations challenges associated with inefficiencies in accessing, integrating, and making data available. It is an orchestration of people, processes, technology to deliver high-quality data to his data citizens fast. DataOps is focused on collaboration to deliver agility, speed, and new data initiatives at scale.

DataOps has 3 objectives –

Know your data

Trust your data

Use your data

This course is part of the Data Engineering Specialization which provides learners with the specialized skills required to be a Data Engineer. It has been estimated millions of jobs will be discovered in 2022 for this role as it is in demand. So its certification is going to play an important role for the complete knowledge and to get a job at good organizations.

Machine learning – Machine learning is a kind of intelligence of system to learn things without taking human help. It is an important part of artificial intelligence to use as a brain that uses data and algorithms to learn new things as well as take decisions.

The Machine Learning certification will help you to master the skills required to become an expert in this domain. Master skills such as Python, ML algorithms, statistics, supervised and unsupervised learning, etc. to become a successful professional in this popular technology.

As the market is changing it has been seen vast growth in this field. In 2022 as well as in the rest of the upcoming years a machine learning will dominate the market.

Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, and SRE courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

DevOpsSchool is one of the best platforms which provides good training as well as a free atmosphere where you get so many benefits to learn.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

VMware updates VMware SASE with VMware Cloud Web Security, a cloud-hosted security service that can be delivered via a global network of VMware SASE and partner points of presence.

VMware Cloud Web Security is designed to protect users and infrastructure accessing SaaS and Internet apps. It says the new platform can respond quickly to changing threats, encryptions, and business needs. Removing scaling difficulties while providing insights to help reduce the attack surface

According to Dell’Oro Group, the SASE market size is estimated to grow by 5X over the next five years. There are a number of factors driving this growth, including:

Applications moving to the cloud and edge.
Employees accessing apps from anywhere as they adopt different work styles.
Data processed, analysed, and stored at the edge is multiplying at a rapid rate.
In order to adapt to these changes, organisations need to adopt both a cloud-centric and an edge-native model, support a hybrid workforce, while protecting against emerging security threats. SASE is becoming a common approach for networking and security across this distributed environment.
“We introduced VMware SASE at VMworld 2020. Since then, we’ve been building a platform that converges industry-leading cloud networking and cloud security services,” says VMware senior vice president and general manager for Service Provider and Edge, Sanjay Uppal.

“At the same time, VMware and our partners have created more than 150 SASE points of presence, which are strategically distributed around the world and serve as an on-ramp to SaaS and other cloud services.”

“VMware SASE combines SD-WAN capabilities with cloud-delivered security functions, including cloud web security, cloud access security broker, zero-trust network access, and AIOps, among others. These capabilities are delivered as-a-service from a global network of PoPs (partner points of presence). VMware Cloud Web Security launched today. We are proud of the progress we’ve made.”

VMware SASE is positioned between the service provider underlay and the application overlay. The platform is able to draw on 5G services to improve latency or performance, while at the same time deliver these capabilities to applications running in the cloud or workloads moving to the edge.

VMware uses drone fleets as an example. It says many logistics companies use drones in large warehouses to help with inventory and restocking. It says there’s no room for error with flight paths as drones fly above employees working on the floor. VMware says low latency and reliable networks are crucial, and SASE platforms can use a 5G underlay service, such as a multi-access edge computing service, to improve the performance of drone management applications delivered as an overlay service.

“If you think about it from this angle, what we are really creating is an extensible service edge platform in the cloud,” says Uppal.

“Businesses can consume services on-demand in a subscription-based model. The pandemic proved we never really know when we’ll have to make a quick pivot in the name of business continuity. An extensible service edge platform would help customers deploy the services they need on the turn of a dime.”

Secure access service edge (SASE) saw rapid growth during 2020 as enterprises grappled with the abrupt and unceremonious shift to remote work in the wake of a global pandemic. But while the technology addressed many of the security challenges associated with a remote work force, it didn’t necessarily solve the problems faced by the workers themselves.

Dropped Zoom calls, applications running at a glacial pace, and bad WiFi have become a fact of life. And these challenges have put a new burden on IT teams tasked with keeping employees connected and productive.

“All the user knows is that ‘my Office 365 access is not working,’ and I have an IT staff that’s generally a small team trained to troubleshoot corporate networks in an enterprise office environment. Now they have all these home users and a huge number of them are calling in asking for help,” explained Craig Connors, CTO of VMware’s SD-WAN and SASE business, in an interview with SDxCentral.

It’s at this juncture that companies like VMware hope artificial intelligence operations (AIOps) platforms can help.

VMware’s SASE Gets Smart
In retrospect, VMware’s timing couldn’t have been better. The company announced the acquisition of AIOps vendor Nyansa a year ago this week. Since then, Nyansa’s Voyance platform, now rebranded as Edge Network Intelligence, has become an integral part of VMware’s SASE.

“The acquisition of Nyansa was made in large part to bring AIOps and this intelligence into SD-WAN to facilitate a move from point-to-point error correction methodologies that we have applied historically in SDN to a broader end-to-end, network-based approach,” Connors said.

Prior to its integration with VMware, Nyansa could pull in real-time and historical data from devices on the network. This information was processed through a series of machine learning and AI algorithms designed specifically to identify and remediate network disruptions.

“Using all of that data, they could build a very robust set of information about what problems were occurring and where on the LAN,” said Connors.

VMware has since taken these capabilities and integrated it into its SD-WAN appliance, he explained. “And because it’s a border gateway between the LAN and the WAN, we’re able to not only detect when application problems and connectivity problems are happening, but we’re able to isolate the fault to whether it is a LAN problem or WAN problem.”

This, he said, is critical because most of the issues remote workers are facing today aren’t actually with their WiFi or LAN. “It’s the fact that when a user has a problem and they call the help desk, the help desk is really at a loss for how to help them troubleshoot the problem and fix it.”

To further empower these capabilities, VMware has begun integrating with key applications like Zoom to gather metrics on call performance. This allows users to get a better picture of how applications are performing.

These capabilities feed into an anomaly detection engine that can help to determine whether there is a LAN issue affecting a single user, a hardware issue affecting users with a specific piece of equipment, or a WAN issue affecting an entire region.

And those insights aren’t siloed to one enterprise customer, said Connors.

“We have this huge user base that are all going through our gateways, so you may not be on a Zoom call yet when we identify that users in your area traversing a certain gateway are having Zoom issues,” he said, adding that traffic can be rerouted around the disruption before an issue occurs.

AIOps Is Becoming a Differentiator
VMware isn’t the only SD-WAN, or even SASE vendor, employing machine learning to identify and remediate disruptions. Open Systems, another SASE vendor, made several acquisitions early last year to bolster its AIOps capabilities, particularly around security.

Meanwhile, both Aruba and Juniper — two companies that recently entered the SD-WAN market with the acquisition of Silver Peak and 128 Technologies respectively — have made steady investments in AIOps capabilities to combat network disruptions and resolve application performance challenges.

However, Connors argues that VMware’s ability to draw in data from its points of presence and use that information to reroute traffic around disruptions is what sets it apart from other vendors.

“If you’re not able to reroute folks through the cloud or apply error corrections across the last mile and give someone a high-quality handoff into Zoom, you can’t fix Zoom problems,” he said, adding that VMWare’s distributed SD-WAN gateways and SASE architecture allows the vendor to do just that.

Digital.ai, the creator of the industry’s first intelligent Value Stream Platform, today announced that it has been granted four U.S. patents for advances in AI-powered analytics. The patents represent industry-leading innovations in applying artificial intelligence (AI), machine learning (ML), and automation techniques to address key challenges business and IT leaders face in driving higher levels of performance within their organizations. These four new patents bring the company’s total to more than 45 patents granted and further establish Digital.ai as the innovation leader in value stream management for the Global 5000.

Digital.ai’s four new patents pertain to the application of artificial intelligence and machine learning techniques to predict change and service disruption risk, in addition to automation innovations embedded in Digital.ai’s Value Stream Platform. According to Gartner’s recent Market Guide of DevOps Value Stream Management Platforms (VSMPs), “predictive analytics will be a key capability offered by DevOps VSMPs,” as “organizations are increasingly embracing the value of analytics to ensure successful deployments and demonstrate reduced production defects.”

“These new patents strengthen the AI-powered engine that serves as the foundation of the Digital.ai Value Stream Platform and give our customers peace of mind regarding the reliability of their application infrastructure,” said Gaurav Rewari, CTO and GM of VSM at Digital.ai. “Given today’s volatility, many enterprises have either slowed down or paused updates to their applications. However, their customers are relying on them to innovate more now than ever before. The capabilities represented by these patents enable IT organizations to continue to rapidly drive value for their customers while mitigating risk to the business.”

Digital.ai’s newest patents include:

Automated Health Assessment and Outage Prediction System (US Patent No. 10,613,525 ) Enables IT departments to increase change velocity while minimizing business service interruptions. The patented techniques use IT service management (ITSM) data in conjunction with application performance management (APM) and machine data to score changes, monitor the impact of changes post execution, and evaluate the health of a configuration item (CI). The technology is part of Digital.ai’s Change Risk Prediction and Major Incident Prediction solutions.

Warehouse-Based Reporting and Operational Reporting Integration (US Patent No. 10,614,091 ) Enables the unification of operational and warehouse reporting for analysis of real time content side by side with historical content, and turns data discrepancies between real-time and historical content into useful information for trend identification and proactive alerting.

Input Gathering System and Method for Defining, Refining, or Validating Star Schema for a Source Database (US Patent No. 10,599,678 ) Enables programmatic analysis of documentation, operational reporting setups, and query logs in conjunction with data profiling for identifying the inputs needed for creating and updating star schemas that are required for warehouse-based reporting. These automated techniques minimize the need for domain experts and enable Digital.ai’s analytic solutions to leverage a large number of data sources that are part of typical value stream chains in a cost-effective manner.

Model-Driven Reporting (US Patent No. 10,713,609 ) Enables programmatic generation of reports for multiple business intelligence (BI) tools using a declarative BI tool agnostic model for reporting which also enables lineage and change impact analysis all the way from data sources to the reporting layer.

The addition of these patents brings Digital.ai’s total to over 45 patents granted with several additional patents pending. The patented technologies are the foundation of Digital.ai’s Value Stream Platform and reflect the company’s expertise across agile, security, DevOps, AIOps and advanced analytics. The Digital.ai intelligent Value Stream Platform provides a unified value stream solution, including enterprise agile planning with continuous delivery and release orchestration, continuous testing and application security, that enables organizations to align software development value streams to strategic business outcomes.

Digital.ai

Digital.ai is an industry-leading technology company dedicated to helping Global 5000 enterprises achieve digital transformation goals. Using value stream management as its cornerstone, Digital.ai combines innovative technologies in agile planning, application protection, software testing and delivery, and artificial intelligence into a unified Value Stream Platform. Digital.ai makes it possible to connect software development and delivery efforts to strategic business outcomes and create secure digital experiences customers trust

Gartner Market Guide for DevOps Value Stream Management Platforms, by Hassan Ennaciri, Manjunath Bhat, Daniel Betts, Chris Saunderson, Joachim Herschmann, and Thomas Murphy- 29-Sept-2020.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All right reserved.

One of the essential goals of DevOps methodology is to establish a more concise approach to software delivery, especially in continuous integration/continuous deployment (CI/CD) environments. As organizations explore this approach, additional elements such as security have been introduced to streamline the cycle. It means developers may have to learn to wear several more hats, especially as some experts look to ways DevOps will continue to mature in 2021.

Many organizations have already moved beyond the initial discovery of learning about and implementing DevOps, says Daniel Betts, senior research director with Gartner, and are now looking at how they can scale their success with the methodology across the enterprise. There are also organizations that may have attempted to adopt DevOps, but need to reset and try again, as well as those who have yet to give it a go, he says.

Organizations that are scaling with DevOps are looking at such additional approaches as Platform Ops, Betts says, which includes techniques to help them scale DevOps as a practice across the organization. “Organizations that embrace that DevOps is about enabling the delivery of the business to give customer value in a much more rapid and efficient way,” he says. The collaboration of IT and business working together through DevOps, Betts says, is more successful than organizations that only focus on technology and tools, who tend to struggle.

DevSecOps, the inclusion of security in DevOps, is dominating thought processes for modern software delivery, says Michael Gilfix, IBM’s vice president of cloud integration and chief product officer for Cloud Paks. IT becomes more decentralized, he says, thanks to the cloud, the need to deal with overwhelming IT backlogs, and the importance of software as a mechanism to deliver business value. “That is accelerating the trend towards an integrated approach to software delivery,” Gilfix says.

In prior times, a developer wrote code, worked with siloed teams to validate that code was secure, ensure that it was compliant with the organization’s needs, and then worked with an operations team to stand up the code in an environment and make sure it works in a consistent manner. “All of those things are being compressed into a single delivery pipeline, aided by modern CI/CD tools,” he says.

The overarching driver of that, Gilfix says, is teams already work in a decentralized manner. The integration of such a process can lead to significant gains in delivery velocity, he says. With many organizations looking to build software reliability engineering competency, the more businesses depend on software to run their core operations, the more critical it is that the software just hast to work, Gilfix says.

Pandemic pivot

Pushed by the pandemic, many businesses have no choice but to rely on their digital channels, he says. As organizations focus on building up reliability and put preventive measures in place, the effort becomes data intensive, Gilfix says. “People have to sift through logs that come from applications and network devices. They have to set up monitoring and alert tools,” he says. “They have to leverage all these various forms of data to figure out where the application is working, and they have to have mature abilities to build a development staging pipeline.”

That means testing the applications, simulating real world needs, and moving change management into product, Gilfix says. Finding skilled professionals capable of performing those tasks quickly with large-scale applications is a challenge. This is where AIOps, the application of artificial intelligence to make sense of that data for DevOps, comes into play, he says. “Issues can be resolved quicker,” Gilfix says. “You can pinpoint similar issues in your applications and fix them preventatively. You can leverage AI to ensure, in a decentralized manner, you’re compliant and manage risk.” AI can also be used to avoid errors downstream in the development process. “The earlier in your software development cycle you can push changes, the more cost effective it is and the better velocity you get.” This “Shift Left” philosophy makes AIOps and DevSecOps highly effective together, Gilfix says.

IBM is a big investor in AIOps technology, he says, which has included bringing an AIOps solution to market in early 2020 that allows developers to establish a baseline of normalcy. This has been expanded to cover use cases such as compliance, risk management, and software quality. IBM also partnered with ServiceNow to integrate its AIOps technology with service management and visibility. “We think AIOps is going to revolutionize application delivery,” Gilfix says. “AIOps can solve that back-end problem such that we can fix a part that has been slowing down delivery teams.”

Even more complexity maybe be coming to DevOps as organizations weigh other aspects of software deployment that could be improved. Performance engineering, for example, is surfacing in the development cycle, says Forrester principal analyst Christopher Condo. As monoliths get broken into microservices, a distributed computing problem may also emerge, he says, where data is shared via APIs. “Now the size of APIs, the amount of calls it takes, where those things are going to be hosted — if they’re not accounted for early in the design phase — that can be bad,” Condo says. Engineers need to ask questions about how many times per second an API will be called, he says, and what happens if a service goes down.

Performance is entering DevOps comparable to the way security entered the cycle and will continue to do so in 2021, Condo says, which raises questions about if too much is being added to a development process meant to be streamlined. “It depends on what the risk of not doing that is,” he says. Organizations might need to assess what may happen if a security analysis is not performed upfront on a software component, deferring to wait until the end. “What is the risk of it being delayed or of having a security issue?” Condo asks. “I can see there possibly being a limit to what is included in the development process. But if you were going to ask what comes after DevSecOps, I would say performance.”

MINNEAPOLIS: Perforce Software, a provider of DevOps solutions for enterprise teams, announced the release of the book, Accelerating Software Quality: Machine Learning and Artificial Intelligence in the Age of DevOps. The announcement was a part of DevOps Next, Perforce’s virtual conference on AI and ML in DevOps.

Spearheaded by Perforce and written by Perfecto Chief Evangelist, Product Manager, and best-selling author Eran Kinsbruner, Accelerating Software Quality covers the basics of AI and ML in software development and testing, and provides practical tips in applying commercial and open source AI/ML tools. In addition to Kinsbruner’s insights and observations, over 20 DevOps industry leaders contributed to how AI and ML will affect existing apps, processes, and tools.

Other topics addressed in the book include chatbot testing, visual-based testing using AI, automated security scanning for vulnerabilities, automated code reviews, API testing and management using AI/ML, reducing effort and time through test impact analysis (TIA), robotic process automation (RPA), AIOps for smarter code deployments and the prevention of production defects, and more.

My latest book provides a complete overview of the use cases for AI and ML in software development and testing, along with practical tools and examples for managers and practitioners looking to enhance their overall productivity, automation, and efficiency. My hope is that readers will be better positioned to make decisions as they adopt AI/ML technologies on their DevOps journey, says Kinsbruner.

Following the release of Continuous Testing for DevOps Professionals A Practical Guide From Industry Experts and The Digital Quality Handbook Guide for Achieving Continuous Quality in a DevOps Reality, Accelerating Software Quality is the third book by Kinsbruner. Together, this set forms a substantial collection of best practices and recommendations for testing, DevOps productivity, and adopting evolving technologies.

BigPanda Inc. and Datadog today announced they will integrate their respective platforms to make it easier to form artificial intelligence for automating IT operations (AIOps) to consume data generated by the Datadog IT monitoring service. The two companies are pledging to invest in a go-to-market partnership to create a comprehensive platform for monitoring, analytics and incident management.

As part of that effort, Datadog is committing to making available an “integration tile” through which BigPanda will also be able to feed root cause analytics based on machine learning algorithms into the Datadog console. That capability will be enabled by a service-to-service topology map created by Datadog that will be used to drive event enrichment, correlation, impact analysis and prioritization.

Mohan Kompella, vice president of product marketing for BigPanda, said Datadog affords his company a unique opportunity to better train AI models using a massive amount of IT and security metrics that Datadog aggregates daily. That data can then be combined with data ingested from other IT service management systems, which will enable IT operations to automate processes using better-trained AI models, added Kompella. Thanks to modern application programming interfaces (APIs), Kompella noted it’s become a lot easier for platforms such as BigPanda to consume a wide range of data.

The more data an AI model is exposed to the faster it learns about the IT environment an organization is seeking to automate. Each IT environment is unique, so it usually takes some time for AI models to be optimally tuned. The more data that’s made available, the faster the AI platform is able to make accurate recommendations. It’s then up to each IT team to decide to what degree they want the AIOps platform to automate a task based on those recommendations versus implementing themselves. The more accurate the recommendations become over time, the more confidence IT teams gain in the AIOps platform.

Of course, IT environments tend to change rapidly and evolve in the DevOps era. The alliance with Datadog will provide BigPanda with a primary source of data from tools that are already widely employed by DevOps teams.

Ultimately, the goal is to reduce the mean time to resolution for IT incidents, in addition to eliminating as much as possible the need to invite developers and IT operations teams to a “war room” meeting to discover the root cause of an issue. Many IT teams these days find themselves spending weeks trying to ascertain the root cause of an issue that, once discovered, only takes a few minutes to fix. AIOps platforms afford IT teams the opportunity to eliminate a lot of friction that currently exists in those complex IT environments that have scaled beyond the ability of humans to manage without some AI assistance.

In the wake of the economic downturn brought on by the COVID-19 pandemic, interest in AIOps is clearly on the rise. IT environments continue to grow in size as the enterprise IT becomes more extended. It won’t be long before most IT teams expect to be able to leverage AI to manage highly distributed computing environments. It may, of course, be a while before IT teams fully trust those platforms; however, it’s also clear IT as a whole is reaching a point where there is no alternative.

The Problem: Kubernetes Was Built for Production, Not for ML Experimentation

If your organization is running machine learning workloads, you have almost certainly run into a familiar tension: data science teams need enormous compute resources, and your Kubernetes cluster is right there. The temptation to hand them cluster access is real. The consequences, however, are predictable.

Kubernetes was engineered for running stable, production-grade, replicated services. ML and deep learning (DL) workloads are almost the exact opposite: they are experimental, ephemeral, highly variable in resource demand, and they change constantly. Pretending otherwise creates operational pain for everyone involved.

What Data Scientists Actually Need

Data scientists are researchers first. Their job is to develop models, tune hyperparameters, and run experiments — often hundreds or thousands of them concurrently. To do that effectively, they need:

• Remote compute with GPU access. Local laptops typically lack the memory, storage, and GPU capacity required for modern deep learning workloads.
• Fast environment iteration. Experiment code changes frequently. Keeping Dockerfiles, requirements.txt files, and Conda YAML manifests up to date manually is a distraction from the actual science.
• Domain-specific monitoring. They care about model accuracy, training loss curves, and experiment throughput — not CPU utilization graphs.
• Queue-based job scheduling. Running many concurrent experiments that are short-lived relative to server uptime is an HPC-style challenge, not a standard Kubernetes workload pattern.

Why Giving Data Scientists Raw Kubernetes Access Is a Bad Idea

On a well-managed Kubernetes cluster, a set of operational rules exist for good reason. Granting unrestricted access to ML teams tends to violate most of them:

• Placing all pods in a single namespace.
• Manually managing large numbers of heterogeneous pod types and repository workloads.
• Attempting to control Kubernetes scheduler ordering — which is not reliably possible.
• Leaving thousands of stale, single-use container images on the cluster.
• Triggering uncontrolled node autoscaling in response to a flood of pending experimental jobs.

Unlike traditional software deployment — where a tested, stable artifact is replicated across nodes — ML experimentation means continuously deploying unstable, in-progress code at scale. A cluster of tens of thousands of one-off container images is not a hypothetical; it is a common outcome.

Breaking Down the Real Requirements

The needs of DevOps teams and data science teams differ significantly across every dimension of infrastructure management. Understanding the gap is the first step toward bridging it.

Resource Access

For a DevOps engineer, resource access means namespaces, RBAC policies, network policies, and reliability. For a data scientist, it means: Is this a CPU machine or a GPU machine, and how many GPUs does it have? A simple tiered model — CPU, 1×GPU, 2×GPU, 8×GPU — typically covers the majority of AI team requirements. The nuance that matters to operations is largely irrelevant to the researchers.

Environment Packaging

From a DevOps perspective, environment packaging means containerizing a stable codebase. For ML teams, the development environment changes constantly. Manually maintaining Dockerfiles and dependency manifests is time-consuming and error-prone — stale configurations accumulate quickly because they are decoupled from the actual coding environment. What is needed is automatic extraction of the environment state, with seamless replication from local development machines to remote execution pods.

Monitoring

DevOps monitoring focuses on infrastructure health: CPU usage, memory pressure, disk I/O, pod restarts. Data science monitoring focuses on experiment health: training loss, validation accuracy, throughput, and custom business-relevant metrics. No universal standard exists for ML experiment monitoring, and the ability to define and track custom metrics is often what separates a productive ML team from one flying blind.

Job Scheduling

Kubernetes job scheduling is oriented around resource allocation for long-running or indefinite workloads. ML job scheduling is closer to an HPC queuing problem: there are always more experiments queued than available resources, jobs are relatively short-lived, and the scheduling policy — which job runs first, on which resources, with what priority — directly affects research velocity. Kubernetes was not designed to optimize for this pattern.

MLOps: A Purpose-Built Layer for AI Workloads

MLOps tooling has emerged specifically to address this gap. Rather than replacing Kubernetes, a well-designed MLOps platform sits on top of it — presenting a resource interface tailored to data science teams while letting Kubernetes continue to do what it does well: managing the underlying hardware cluster.

From a Kubernetes perspective, the MLOps platform is just another application: a replicated service with a known resource footprint, deployed and managed like anything else on the cluster. From the data science team’s perspective, it exposes their allocated compute nodes as a logical cluster they control directly.

What an MLOps Platform Provides

• Dedicated job queues with priority ordering, experiment tracking, and isolation from Kubernetes scheduler internals.
• Automatic environment packaging that extracts and replicates the current development environment into a container without requiring the data scientist to manually maintain Dockerfiles.
• Experiment-centric monitoring with customizable metrics dashboards suited to model development workflows.
• Lifecycle management for experiments, including the ability to terminate runaway jobs without DevOps intervention.
• Self-service provisioning so that the AI team can launch, monitor, and clean up their own workloads within the resource budget already allocated by the DevOps team.

The Resulting Division of Responsibility

With an MLOps layer in place, the operational model becomes significantly cleaner:

• DevOps team: Manages Kubernetes, allocates hardware resources to the MLOps platform, and updates resource allocations when needed. They are not in the critical path for every experiment run.
• Data science team: Self-services through the MLOps platform. They launch experiments, track results, manage their queues, and iterate on models without needing to understand Kubernetes internals or file infrastructure tickets.

The MLOps solution acts as the glue: it picks jobs from the ML team’s queues, provisions the appropriate environment (inside a container or as a sidecar), submits the workload to Kubernetes with proper resource requests, monitors execution, and handles cleanup — all without manual DevOps involvement.

Key Takeaways

• Kubernetes is the right tool for managing production infrastructure and hardware clusters. It is not the right tool for managing ML experiment workflows directly.
• Data science teams have fundamentally different requirements for resource access, environment management, monitoring, and scheduling than traditional software engineering teams.
• MLOps platforms address these requirements by providing a purpose-built interface that runs on top of Kubernetes without disrupting its operational model.
• A clean MLOps integration reduces the operational burden on DevOps teams, eliminates cluster sprawl, and gives data scientists the autonomy they need to iterate quickly.

The payoff is straightforward: fewer 2 a.m. provisioning calls, fewer stale container images clogging your registry, and a data science team that can actually focus on building models rather than filing infrastructure tickets.

Why Kubernetes and DevOps Work Well Together

DevOps covers the full software lifecycle—from planning and coding through testing, deployment, and monitoring. Kubernetes addresses the operational half of that cycle directly, minimizing manual infrastructure work while improving automation, scalability, and application resiliency.

Kubernetes is built on a service-oriented, object-oriented architecture, which aligns naturally with Agile principles. Teams can encode policies for scaling and resiliency into Kubernetes configuration rather than managing those concerns by hand in production. The result is that engineers spend less time on routine operational tasks and more time delivering features.

The benefit is especially visible in multi-environment organizations. Without a consistent orchestration layer, deployment practices for on-premises infrastructure and public cloud diverge quickly. Kubernetes provides the same control plane and the same declarative configuration model regardless of where containers run, giving DevOps teams a consistent deployment experience across environments.

Key Benefits Kubernetes Brings to a DevOps Toolchain

Infrastructure Abstraction

Kubernetes abstracts the underlying hardware and cloud resources so that developers and operators can deploy applications without caring whether the workload lands on bare metal, a private cloud, or a public cloud provider. A container runs identically inside Kubernetes regardless of the host, which eliminates environment-specific deployment logic and reduces the classic “it works on my machine” class of problems.

Everything as Code

Kubernetes treats infrastructure and application configuration as code. Resources are described in declarative, portable YAML or JSON manifests that can be version-controlled in a Git repository alongside application code. A configuration change—such as a rolling update or a scaling rule—is written into a manifest and applied through the Kubernetes API rather than executed as a manual, undocumented action. This approach integrates naturally with GitOps workflows and makes configuration auditable and reproducible.

Advanced Deployment Techniques

Kubernetes enables deployment patterns that are difficult to implement manually:

• Rolling updates – deploy a new version incrementally, replacing old pods without downtime.
• Canary releases – route a small percentage of traffic to a new version to validate behavior in production before a full rollout.
• Blue/green deployments – maintain two identical environments and switch traffic between them.
• Rollbacks – revert to a previous deployment version with a single command if a release introduces regressions.

These techniques let developers ship changes quickly and with confidence—a core goal of any DevOps strategy.

Immutability

Kubernetes does not modify running containers. Instead, when a change is needed, the old container is stopped and a new one is started from an updated image. This immutability model reduces configuration drift, simplifies debugging, and makes deployments predictable. For operations teams, it means a misbehaving container can be redeployed or replaced with minimal disruption to surrounding services.

Helm Charts for Reusable Packaging

Helm is the standard package manager for Kubernetes. A Helm chart is a reusable, templated description of the Kubernetes resources an application requires. Operations teams can use a single chart to deploy the same application across multiple projects or environments, customizing values at deploy time rather than maintaining separate manifests for each target. Without Helm—or a similar templating approach—teams end up duplicating and diverging YAML across environments.

CI/CD Integration

Because Kubernetes is open source and managed by the Cloud Native Computing Foundation (CNCF), it integrates with the broad ecosystem of CI/CD tools that DevOps teams already use—Jenkins, GitLab CI, GitHub Actions, Argo CD, Tekton, and others. Pipelines can build a container image, push it to a registry, update a Kubernetes manifest, and trigger a deployment automatically, with no manual intervention between a code merge and a production release.

Kubernetes Architecture Concepts Every DevOps Engineer Should Know

Understanding how Kubernetes is structured helps DevOps teams make better architectural decisions:

• Containers – the smallest deployable unit, packaging an application and its dependencies.
• Pods – the basic Kubernetes object, wrapping one or more tightly coupled containers that share network and storage. Multiple pod replicas can run in parallel for scaling and fault tolerance.
• Nodes – the worker machines (virtual or physical) that run pods. Nodes are the compute layer.
• Clusters – a group of nodes managed by a Kubernetes control plane. The cluster abstracts the physical or virtual hardware from the workloads running on it.
• Control plane – the set of components (API server, scheduler, controller manager, etcd) that manage cluster state and make scheduling decisions.

Kubernetes can run on-premises, in public cloud environments (as a managed service such as GKE, EKS, or AKS), or as a hybrid of both. Managed cloud offerings reduce the operational burden of maintaining the control plane, making it easier for smaller teams to adopt Kubernetes without deep cluster-administration expertise.

Risks and Challenges of Kubernetes for DevOps Teams

Kubernetes is not without real costs and risks. Teams evaluating adoption should weigh the following carefully.

Steep Learning Curve and Talent Shortage

Kubernetes is a genuinely complex system. Concepts such as networking (Services, Ingress, CNI plugins), storage (PersistentVolumes, StorageClasses), RBAC, and custom resource definitions take time to learn and longer to master. Organizations should invest in structured training rather than relying solely on proof-of-concept projects to build skills. Running a small pilot is valuable for exploration, but it does not produce the production-operations competency a team needs when running Kubernetes at scale.

Multi-Tenancy Complexity

When multiple teams or business units share a Kubernetes cluster, isolation becomes a significant concern. Each tenant needs its own namespace, RBAC policies, network policies, and resource quotas so that one workload cannot consume resources at the expense of others or access another tenant’s logs, metrics, or metadata. Managing this correctly requires deliberate architectural planning. Organizations sometimes find it simpler—though more expensive—to run separate clusters per team or environment rather than enforce rigorous multi-tenancy on a shared cluster.

Resource Overprovisioning

Poor resource request and limit configuration is one of the most common operational problems in Kubernetes. Teams that do not tune CPU and memory requests relative to actual workload behavior end up overprovisioning nodes—dedicating capacity for peak loads that rarely materialize—which drives up cloud spend or wastes owned hardware capacity. Right-sizing requires ongoing measurement and adjustment, not a one-time configuration decision.

Operational Overhead

Running Kubernetes in production means operating additional infrastructure (the control plane and supporting services such as monitoring, logging, and ingress controllers) on top of the applications the platform hosts. For small teams or simple applications, this overhead may outweigh the benefits. Managed Kubernetes services reduce but do not eliminate this burden.

The Future: Hybrid Infrastructure and Distributed Orchestration

The DevOps toolchain has long emphasized automation through CI/CD pipelines and scripted deployments. Kubernetes extends that automation to infrastructure provisioning, scaling, self-healing, and traffic management—concerns that previously required manual operator intervention or bespoke scripting.

Looking ahead, most enterprise environments will remain hybrid: legacy applications in on-premises data centers, existing workloads in the cloud, and new applications cloud-native from the start. Kubernetes is positioned to serve as the consistent orchestration layer across all three, supporting DevOps teams and their users regardless of where workloads run or where engineers work.

The organizations that invest now in building genuine Kubernetes expertise—not just familiarity—will be better positioned to operate increasingly distributed, hybrid application estates efficiently and securely.

Original source reference: searchitoperations.techtarget.com

The Core Challenge: Too Much Data, Too Little Time

Modern IT teams operate under an uncomfortable contradiction. The systems they maintain have become business-critical — when apps go down, revenue disappears and customers churn — yet the sheer volume of telemetry data those systems generate has made manual monitoring impractical. Mainframes gave way to distributed computing, which gave way to virtualisation, which gave way to microservices and ephemeral containers. Each transition multiplied the number of signals an operations team must interpret.

Legacy monitoring tools were designed for simpler, more static environments. Today they produce an overwhelming flood of alerts, most of which are noise, while the genuinely actionable signals are buried underneath. The result: teams spend more time reacting to emergencies than building new capabilities, agility stalls, and innovation shrinks.

How DevOps Tried to Solve the Problem

The DevOps movement tackled this challenge by re-centering ownership. Rather than handing off services to a centralised operations team with little product context, DevOps places responsibility for reliability, deployment, and support directly on the engineers who write the code. This decentralises operations into smaller, empowered squads, each accountable for its own microservice and its contribution to the overall customer experience.

Many organisations complement this model with a Site Reliability Engineering (SRE) function. SREs monitor observability data, identify potential failure modes early, and feed those insights back into the development cycle — strengthening reliability and scalability before incidents reach production.

DevOps and SRE practices represent genuine progress. However, they surface a new set of coordination problems as organisations scale.

Where DevOps Teams Still Struggle

Even well-run DevOps organisations encounter friction at the boundaries between teams and tools:

• Alert noise at scale. Each microservice emits its own telemetry. Across dozens or hundreds of services, surfacing the events that actually matter — and understanding how they relate to each other — requires significant cognitive effort.
• Unclear blast radius. When an incident occurs, understanding which downstream services and customers are affected is rarely straightforward. Teams need relationship context, not just raw metrics.
• Cross-team coordination. DevOps squads interact primarily through APIs. When a multi-service incident demands urgent collaboration from geographically dispersed teams with different on-call schedules and escalation paths, orchestrating the right response quickly is genuinely hard.
• Post-incident learning. Without a structured feedback loop, teams repeat the same investigations and make the same mistakes. Post-mortems are often rushed or skipped under the pressure of the next deployment.

These are not failures of DevOps as a philosophy — they are the natural consequences of operating at a scale that exceeds what human attention alone can manage.

What Is AIOps?

Artificial Intelligence for IT Operations (AIOps) refers to platforms that apply machine learning and data analytics to the monitoring, event correlation, and incident response workflows that operations and SRE teams depend on. The term was popularised by analyst firm Gartner, though the underlying techniques — anomaly detection, topology mapping, noise reduction, root cause analysis — have been evolving for years.

A mature AIOps platform typically provides several capabilities:

• Baseline learning. The platform observes normal behaviour across your environment over time, so it can distinguish a genuine anomaly from expected variation.
• Alert correlation and noise reduction. Related alerts from multiple sources are grouped into a single, actionable incident rather than flooding dashboards with thousands of individual events.
• Root cause identification. By mapping dependencies between services and infrastructure components, AIOps can point to the probable origin of an incident rather than simply listing its symptoms.
• Prescriptive recommendations. Drawing on historical resolution data, the platform can suggest remediation steps, shortening the time from detection to fix.
• Intelligent notification routing. The right people are engaged at the right time, respecting on-call schedules, escalation paths, and geographic constraints.
• Collaborative workspaces. Team members share a consistent, real-time view of the incident, its context, and the actions being taken.
• Post-mortem acceleration. Structured capture of resolution steps and learnings feeds future incident prediction and speeds future response.

How AIOps Complements DevOps

DevOps and AIOps are not competing philosophies. AIOps is best understood as the intelligence layer that makes DevOps practices sustainable at enterprise scale.

Cutting Through the Noise

SREs and DevOps engineers should spend their attention on meaningful signals. AIOps handles the initial filtering — correlating alerts across tools and services, suppressing duplicates, and surfacing only the events that warrant human action. Engineers engage with context, not chaos.

Closing the Gap Between Detection and Resolution

By identifying probable root causes and suggesting remediation steps drawn from previous incidents, AIOps shortens mean time to resolution (MTTR). Teams do not start each incident investigation from scratch; they begin with a working hypothesis informed by machine learning applied to historical patterns.

Orchestrating Cross-Team Response

When an incident spans multiple services and requires engineers from multiple teams, AIOps can handle the coordination logic: who is on call, what their escalation path is, which communication channel they prefer. This removes a significant manual burden from incident commanders and reduces response lag.

Building Institutional Memory

Post-mortems generate valuable data that typically lives in a document nobody reads. AIOps platforms ingest resolution steps and feedback, making that knowledge available during future incidents through predictive analysis. Repetitive problems become detectable — and preventable — before they cause outages.

Enabling Edge-Based Intelligence

For AIOps to be effective in high-volume environments, anomaly detection should happen close to where data is generated — at the edge — rather than centralising every raw event before analysis. This reduces data transport costs and latency, and allows the platform to act on local patterns without waiting for a central aggregation step.

Practical Considerations for Teams Evaluating AIOps

Organisations considering an AIOps investment should evaluate platforms against a few practical criteria:

• Integration breadth. An AIOps platform is only as useful as the data it can ingest. Look for broad, well-maintained integrations with your existing monitoring, logging, tracing, ITSM, and communication tools.
• Time to value. Machine learning models need training data. Understand how long a platform takes to learn your environment before it produces useful signal, and what the experience looks like during that ramp-up period.
• Explainability. Engineers need to trust the platform’s recommendations. Opaque black-box correlations are harder to act on and harder to validate. Prefer platforms that can explain why they grouped events or suggested a remediation step.
• Feedback loops. The platform should improve as your team provides feedback — marking correlations as incorrect, flagging missed incidents, confirming root cause assessments. Without this loop, the model stagnates.
• Organisational readiness. AIOps adoption is a cultural and process change as much as a technical one. Teams need to trust automated triage enough to act on it, and to contribute structured feedback. Plan for enablement and change management.

The Bottom Line

DevOps represents a genuine and necessary evolution in how organisations build and operate software. But the scale of modern distributed systems — and the volume of observability data they produce — exceeds what human teams can manage without assistance. AIOps provides that assistance: not to replace engineers, but to remove the work that should never have required engineering judgement in the first place.

When AIOps handles noise reduction, correlation, root cause identification, and notification routing, DevOps and SRE engineers are freed to do what they do best: understand systems deeply, design for reliability, and build experiences that serve customers. That is the argument for DevOps and AIOps working together — not as competing strategies, but as complementary layers of a mature operations practice.

What the Gartner Magic Quadrant Measures

Gartner defines the Application Performance Monitoring (APM) market as software that facilitates application monitoring across three primary functional dimensions:

• Front-end monitoring – tracking the end-user experience in browsers and mobile apps.
• Application discovery, tracing, and diagnostics (ADTD) – mapping application topology, distributed tracing, and deep-dive diagnostics.
• Analytics – aggregating and interpreting performance data to surface actionable insights.

In the 2020 edition of the Magic Quadrant for APM, Gartner recognized four vendors as Leaders — the same four that led the 2019 report. The continuity signals that these platforms have built durable, mature capabilities that competitors in the Niche and Visionary quadrants are still working to replicate.

The four Leaders, listed below in alphabetical order, differ in market share, deployment model, and feature emphasis. Understanding those differences is essential when evaluating which tool fits your organization’s cloud strategy and engineering workflows.

1. AppDynamics

AppDynamics is a full-stack APM suite owned by Cisco. It is designed to improve digital experiences for both customers and internal users by giving engineering teams granular visibility into application behavior across multi-cloud environments.

Key capabilities

• Code-level visibility – traces performance down to individual lines of code and critical business transactions.
• End-user monitoring (EUM) – captures real-user data from browsers and mobile devices to correlate front-end experience with back-end performance.
• Infrastructure visibility – monitors servers, containers, and cloud resources alongside application metrics.
• Business performance monitoring – ties technical KPIs to business outcomes such as conversion rates and revenue impact.

AppDynamics is a common choice in large enterprise environments where aligning IT performance to business metrics is a priority.

2. Broadcom DX APM

Broadcom DX APM (formerly CA APM) is built on a microservices architecture, which gives it flexibility for large, distributed deployments. It is part of Broadcom’s broader DX Platform for IT operations management.

Key capabilities

• Microservices-based agent architecture – individual services can be scaled independently, avoiding the resource overhead of a monolithic agent.
• Cluster Management console – provides a single pane of glass for hardware utilization, service scaling, and onboarding new monitored projects.
• APM Command Center – enables centralized agent upgrades and configuration changes across the estate.
• Operational intelligence and app experience analytics – surfaces trends and anomalies across application and infrastructure layers.

DX APM tends to appeal to enterprises already invested in the Broadcom (formerly CA Technologies) ecosystem for IT operations and service management.

3. Dynatrace

Dynatrace markets itself as a Software Intelligence Platform rather than a point APM solution. It combines observability, AIOps, and digital experience management into a unified product.

Key capabilities

• Automatic discovery and instrumentation – Dynatrace’s OneAgent automatically discovers services, processes, and dependencies without requiring manual configuration.
• PurePath Technology – captures end-to-end distributed traces with timing and code-level context for every transaction across every tier, from the browser through to the database.
• Containerized microservices monitoring – detects and monitors microservices running in Kubernetes and other container orchestrators, and surfaces performance degradations automatically.
• AIOps (Davis AI engine) – correlates events and performs root-cause analysis automatically, reducing alert noise.
• Digital business analytics – connects user behavior data to conversion and revenue metrics.

Dynatrace is a popular choice for cloud-native and Kubernetes-heavy environments where automated observability at scale is a core requirement.

4. New Relic APM

New Relic APM is part of the broader New Relic observability platform. It is well regarded for its breadth of language support and the depth of transaction-level detail it surfaces for developers.

Key capabilities

• Auto-instrumentation for multiple languages – New Relic supports auto-instrumentation for a wide range of programming languages (including Java, .NET, Node.js, Python, Ruby, PHP, and Go as of the 2020 timeframe), reducing the setup burden on engineering teams.
• In-depth transaction tracing – shows exact method calls with line numbers and highlights external dependencies, making root-cause analysis faster.
• Full-stack visibility – provides a single view of the application, its infrastructure, and the end-user experience.
• Distributed tracing – follows requests across microservices and third-party services to surface latency bottlenecks.

New Relic’s pricing model and developer-friendly tooling make it a common choice for SaaS companies and teams that prioritize fast time-to-value.

Comparing the Four Leaders at a Glance

What to Consider When Evaluating APM Tools

Being named a Gartner Magic Quadrant Leader is a meaningful signal, but it should be the starting point of an evaluation rather than the conclusion. Consider the following factors when selecting an APM platform:

• Language and framework support – confirm that your primary runtimes are supported with auto-instrumentation, not just manual SDKs.
• Deployment model – SaaS, on-premises, and hybrid options vary by vendor; verify alignment with your data residency and compliance requirements.
• Pricing model – APM pricing can be per host, per user, per data ingested, or a combination. Model costs against your actual environment before committing.
• Integration ecosystem – check compatibility with your CI/CD pipelines, incident management tools (PagerDuty, Opsgenie), and cloud providers.
• Alerting and AIOps maturity – evaluate how well the platform reduces alert fatigue compared to a threshold-only approach.
• Time to value – agent-based auto-discovery dramatically reduces onboarding time; assess how quickly you can instrument a new service in your stack.

Running a proof-of-concept (PoC) against a representative sample of your production services remains the most reliable way to compare platforms before making a long-term commitment.

Source: Solutions Review coverage of the Gartner Magic Quadrant for Application Performance Monitoring, 2020.

Moogsoft Enterprise 8.0: What Changed

Moogsoft updated its AIOps platform in May 2020 with version 8.0 of Moogsoft Enterprise, targeting IT organizations that need to operate a virtual network operations center (NOC) without requiring staff to be physically on-premises. The release focused on three areas: new third-party integrations, noise-reduction improvements, and a reworked user interface.

New Integrations

• AWS Firelens: Added support for ingesting EC2 log files via Amazon’s Firelens log routing service, extending Moogsoft’s reach into AWS-native observability data.
• PagerDuty: Integration with the widely used incident management platform allows alert data to flow into Moogsoft’s aggregation layer.
• Opsgenie (Atlassian): The Atlassian-owned on-call and incident management tool was added as another data source, broadening coverage for teams already invested in the Atlassian ecosystem.

Noise Reduction and Alert Prioritization

According to Adam Frank, senior product manager at Moogsoft, version 8.0 applies machine learning algorithms to aggregated alerts from multiple platforms with the goal of surfacing only the highest-priority issues. The platform uses noise-reduction analytics to cut through alert storms, and an automated topology visualization tool is included to help engineers trace issues back to their root cause more quickly.

Workflow Engine and UI Improvements

Moogsoft also revamped its user interface and enhanced its Workflow Engine (WE) in this release. The stated aim was to reduce friction when aggregating data from heterogeneous IT management platforms, streamlining the configuration steps required to connect disparate tools.

The Virtual NOC Use Case

Traditional NOC tooling was largely designed with the assumption that operations staff would be working from a central, on-premises location. The COVID-19 pandemic in early 2020 forced many IT teams to operate entirely remotely, exposing limitations in legacy monitoring architectures that depended on physical proximity to dashboards and infrastructure.

Moogsoft’s positioning for version 8.0 leaned directly into this shift. By aggregating alert and incident data from cloud and SaaS platforms into a single pane of glass accessible from anywhere, the platform aims to give distributed teams the situational awareness they would normally get from a centralized NOC.

The Broader AIOps Debate

The release also reflected a continuing strategic tension in the IT management market. Established monitoring and ITSM vendors argue that machine learning should be applied to data where it already lives — within their own platforms. Dedicated AIOps vendors like Moogsoft counter that meaningful AI-driven insights require data to be aggregated across all platforms first, making a neutral aggregation layer essential.

Neither approach is universally correct — the right choice depends heavily on how homogeneous or fragmented an organization’s toolchain already is. Teams running a predominantly single-vendor stack may find embedded AI sufficient. Teams operating across many tools and cloud providers are more likely to benefit from a purpose-built aggregation platform.

Implications for IT Teams

Frank noted that the pandemic was likely to accelerate AIOps adoption for two related reasons: budget pressure reducing the size of IT operations teams, and reduced tolerance for manual, repetitive monitoring tasks. Both pressures push organizations toward automation and AI-assisted triage.

For DevOps and SRE teams evaluating AIOps platforms, version 8.0’s additions are worth noting for a few practical reasons:

• PagerDuty and Opsgenie integration means teams that already use those tools for on-call management can feed that incident data into Moogsoft without ripping out existing workflows.
• AWS Firelens support is relevant for organizations running workloads on EC2 that want log data to participate in ML-driven correlation alongside metrics and events.
• Topology visualization addresses a common pain point in large environments where the blast radius of an incident is hard to scope quickly from raw alert lists alone.

Summary

Moogsoft Enterprise 8.0 expanded the platform’s integration surface and sharpened its alert-prioritization capabilities at a time when remote IT operations were becoming a necessity rather than an option. The additions of AWS Firelens, PagerDuty, and Opsgenie integrations, combined with topology visualization and Workflow Engine improvements, position the platform as a central aggregation point for distributed operations teams managing complex, multi-cloud environments.

New Relic AI: AIOps Reaches General Availability

New Relic, a leading cloud-based observability company, announced the general availability of New Relic AI, its AIOps (Artificial Intelligence for IT Operations) platform. The product is positioned as a core pillar of the broader New Relic One observability platform, extending its capabilities beyond traditional Application Performance Monitoring (APM) and infrastructure monitoring into AI-driven incident intelligence.

Background: The SignifAI Acquisition

The foundation of New Relic AI traces back to February 2019, when New Relic acquired SignifAI, a Silicon Valley-based startup headquartered in Sunnyvale, California, with additional offices in Tel Aviv. SignifAI specialized in applying AI and machine learning to IT operations — precisely the capability New Relic needed to enter the AIOps space. The acquisition allowed New Relic to integrate intelligent, ML-powered operations directly into its observability stack.

What Is New Relic AI?

New Relic AI is described as an open incident correlation and intelligence solution that is source-agnostic and data-agnostic. It integrates deeply with the New Relic One platform and is designed to help IT and DevOps teams detect anomalies, correlate incidents, and reduce the volume of noisy alerts that routinely overwhelm operations teams.

Key capabilities include:

• Anomaly detection: Evaluates telemetry data from disparate sources to surface outliers in patterns before they become critical incidents.
• Incident correlation: Automatically groups related alerts and events to reduce duplicate notifications and alert fatigue.
• Multi-signal ingestion: Ingests and analyzes alerts, logs, metrics, and deployment events from multiple data types simultaneously.
• Contextual insights: Provides an intelligent summary of each incident alongside a live view of ingested data, enabling faster diagnosis and prioritization.
• User feedback tuning: Allows operators to tune correlation rules based on feedback, improving accuracy over time.

The Role of NRDB (New Relic Database)

New Relic AI is powered by NRDB (New Relic Database), a purpose-built, distributed time-series database designed to store telemetry data — metrics, events, logs, and traces — from a wide variety of sources in a uniform format. NRDB is engineered for high-performance querying, making it well-suited for the real-time analysis that AIOps requires. Machine learning algorithms run against data stored in NRDB to identify patterns, detect anomalies, and derive context-based insights.

Reducing Alert Fatigue: What New Relic Claims

Alert fatigue is a well-documented problem in DevOps and IT operations: too many alerts, too many false positives, and too little context lead to slower incident response and missed issues. New Relic AI addresses this by correlating alerts across data sources to filter signal from noise automatically.

According to New Relic, early access customers reported seeing automatic reductions in alert noise of up to 50 percent through incident correlation. This figure is a vendor-reported claim from early access users and has not been independently verified.

Integrations: Works With Your Existing Stack

A notable differentiator for New Relic AI is its broad integration ecosystem. Rather than requiring teams to rip and replace existing tooling, the platform is designed to augment and connect with commonly used monitoring, alerting, and collaboration tools. Supported integrations at launch included:

• Monitoring & observability: Splunk, Prometheus, Grafana, Amazon CloudWatch, Google Cloud’s Stackdriver (now Google Cloud Operations Suite)
• Incident management & on-call: PagerDuty, OpsGenie, VictorOps
• IT service management: ServiceNow
• Collaboration: Slack

By integrating with both public cloud monitoring platforms and third-party tooling, New Relic AI positions itself as a correlating intelligence layer that sits above existing data sources rather than replacing them.

New Relic’s Broader Strategic Shift

New Relic began as an Application Performance Monitoring (APM) company but has progressively expanded its scope. The consolidation of APM and infrastructure monitoring into the unified New Relic One platform was a pivotal step, and New Relic AI represents the next layer — bringing machine learning and AI-driven intelligence to the data already flowing through that platform.

The company’s stated goal is to move beyond reactive monitoring toward proactive, AI-assisted operations — a requirement increasingly driven by the complexity of cloud-native, hybrid, and multi-cloud environments.

The AIOps Market Consolidation

New Relic’s move into AIOps mirrors a broader consolidation trend across the industry. Around the same period, several notable acquisitions reshaped the AIOps and observability landscape:

• Splunk acquired SignalFx
• LogicMonitor acquired Unomaly
• ServiceNow acquired Loom Systems
• VMware acquired Voyance
• New Relic acquired SignifAI

This pattern reflects an industry-wide recognition that observability data alone is not enough — operations teams need intelligent correlation and context to act quickly at scale. AIOps has emerged as the discipline that bridges raw telemetry with actionable incident intelligence.

Summary

New Relic AI’s general availability marks a significant step in New Relic’s evolution from an APM vendor to a full-stack observability and AIOps provider. For DevOps and IT operations teams managing complex, distributed systems, the platform offers a way to centralize alert correlation, reduce noise, and surface actionable insights faster — without abandoning existing monitoring tooling. As multi-cloud and cloud-native architectures continue to grow in complexity, AI-assisted operations platforms like New Relic AI are likely to become a standard part of the enterprise DevOps toolkit.

What Is AIOps and Why Does It Matter?

AIOps — short for Artificial Intelligence for IT Operations — refers to platforms that combine big data, machine learning, and analytics to automate and enhance IT operations tasks. These tasks include event correlation, anomaly detection, root-cause analysis, and incident management. As enterprise IT environments grow more complex — spanning on-premises infrastructure, multiple clouds, containers, and microservices — traditional monitoring tools struggle to keep pace with the volume and velocity of operational data. AIOps platforms aim to close that gap by filtering noise, surfacing actionable insights, and enabling faster incident response.

Key Vendors in the Global AIOps Platforms Market

The market research report identified the following companies as notable participants in the global AIOps Platforms space as of 2020:

• Splunk — Known for its data platform and ITSI (IT Service Intelligence) product, which applies machine learning to operational data for predictive alerting and service health monitoring.
• Cisco (AppDynamics) — AppDynamics provides application performance monitoring (APM) with AI-driven analytics, acquired by Cisco in 2017.
• Micro Focus — Offers IT operations management (ITOM) tooling with AIOps capabilities through its Operations Bridge product line.
• Zenoss — Provides hybrid IT monitoring and AIOps capabilities, with a focus on dynamic service models and predictive analytics.
• Moogsoft — A specialist AIOps vendor focused on alert noise reduction and incident correlation using machine learning.
• BigPanda — Focuses on event correlation and alert enrichment to reduce mean time to detect (MTTD) and mean time to repair (MTTR).
• LogicMonitor — A cloud-based infrastructure monitoring platform with automated anomaly detection.
• ScienceLogic — Targets hybrid IT and service provider environments with AI-driven topology and event management.
• Microsoft — Azure Monitor and related services incorporate AI and machine learning for cloud-native operations monitoring.
• Appnomic AppsOne — Focused on application-aware AIOps, particularly for performance management.
• Autointelli — Provides intelligent automation for IT operations, including runbook automation and event management.
• CloudFabrix — Offers a data-centric AIOps platform targeting observability pipelines and data fabric for IT operations.
• Federator.ai — Specializes in AI-driven workload prediction and resource optimization, particularly for Kubernetes environments.

Market Segmentation

By Deployment Model

AIOps platforms are typically available in two deployment configurations:

• Cloud-based (SaaS) — Hosted and managed by the vendor. Preferred by organizations seeking rapid deployment, lower upfront cost, and automatic updates. Increasingly the dominant delivery model.
• On-premises — Deployed within the customer’s own data center. Preferred by enterprises with strict data residency requirements, regulated industries, or air-gapped environments.

By Organization Size

• Large Enterprises — Typically the early adopters of AIOps, given the scale and complexity of their IT estates and their greater capacity to invest in enterprise tooling.
• Small and Medium Enterprises (SMEs) — Adoption among SMEs has grown as cloud-delivered AIOps reduces implementation complexity and entry cost.

Regional Market Breakdown

The market research covered the following geographic regions:

• North America — Historically the largest regional market, driven by high IT spending, early cloud adoption, and concentration of leading AIOps vendors.
• Europe — Strong demand driven by digital transformation initiatives and regulatory pressure on IT reliability and security.
• China — Significant and growing market with both domestic and international vendors competing for share.
• Rest of Asia Pacific — Includes markets such as Japan, Australia, India, and Southeast Asia, with varied maturity levels.
• Latin America — An emerging market with growing cloud adoption underpinning AIOps interest.
• Middle East & Africa — Adoption is earlier-stage but growing, particularly in Gulf region enterprise and government IT.

Market Drivers

Several structural trends were identified as driving AIOps adoption:

• Explosion of operational data — Modern IT environments generate far more logs, metrics, traces, and events than human operators can analyze manually. Machine learning is increasingly necessary to extract signal from this noise.
• Hybrid and multi-cloud complexity — Managing workloads across on-premises, private cloud, and multiple public clouds creates monitoring blind spots that AIOps platforms are designed to address.
• DevOps and SRE adoption — Organizations practicing DevOps and Site Reliability Engineering (SRE) require faster feedback loops and automated remediation, both key AIOps use cases.
• Cost pressure on IT operations — AIOps can reduce the manual effort required for incident triage and resolution, helping teams do more with fewer resources.
• Growing tolerance for AI-driven automation — As ML models mature and interpretability improves, IT teams are more willing to act on AI-generated recommendations.

Market Challenges

The report flagged several challenges that could constrain market growth:

• Data quality and integration complexity — AIOps platforms depend on high-quality, normalized data from across the IT estate. Integrating data from legacy monitoring tools, diverse cloud APIs, and on-premises infrastructure is a significant implementation challenge.
• Skills gap — Effectively deploying and tuning AIOps platforms requires a combination of IT operations domain knowledge and data science literacy that many teams lack.
• Proving ROI — Quantifying the business value of reduced alert noise or faster MTTR can be difficult, making budget justification challenging.
• Vendor fragmentation — The AIOps landscape is crowded, with significant variation in platform scope, maturity, and underlying approaches. Evaluating and selecting the right platform requires substantial effort.
• Organizational resistance — Automating IT operations decisions can encounter cultural resistance from teams accustomed to manual processes and concerned about job displacement.
• Model transparency — Many AIOps platforms use black-box ML models. Lack of explainability can erode trust among operations engineers who need to understand why an alert was raised or suppressed.

Competitive Landscape

The AIOps market in 2020 was characterized by a mix of established IT management vendors extending their platforms with AI capabilities (Splunk, Micro Focus, Microsoft) and specialist pure-play AIOps vendors (Moogsoft, BigPanda, Federator.ai). Competition was intensifying on several fronts:

• Platform breadth versus depth — Larger vendors offer broad integrations and bundled licensing, while specialists often provide deeper ML capabilities in specific domains such as alert correlation or capacity forecasting.
• Mergers and acquisitions — The report noted M&A activity as a key competitive dynamic, with larger players acquiring specialist AIOps capabilities to round out their portfolios.
• Ecosystem integration — Platforms that integrate tightly with popular DevOps toolchains (Jira, PagerDuty, ServiceNow, Slack) and cloud-native observability stacks (Prometheus, Grafana, OpenTelemetry) tend to see faster adoption.

Research Methodology Used in the Original Report

According to the source, the original market research employed the following analytical frameworks:

• PESTLE analysis — Examining Political, Economic, Social, Technological, Legal, and Environmental factors influencing the market.
• Porter’s Five Forces analysis — Assessing competitive intensity, supplier power, buyer power, threat of substitutes, and barriers to entry.
• Primary and secondary research — Combining vendor interviews and surveys with desk research across industry publications and financial filings.

Key Questions for AIOps Platform Evaluators

Whether you are evaluating the AIOps market for investment purposes or assessing platforms for your own organization, the following questions are worth addressing:

1. Which data sources does the platform ingest natively, and how does it handle custom or legacy sources?
2. How does the platform explain its ML-driven recommendations to operations engineers?
3. What is the typical time-to-value for a deployment of similar scale and complexity?
4. How does the vendor price the platform — by data volume, host count, users, or modules?
5. How does the platform integrate with your existing ITSM, alerting, and on-call tooling?
6. What remediation or automation capabilities are built in, and what requires external orchestration?
7. How does the vendor’s roadmap address cloud-native and FinOps use cases?

Summary

The AIOps platforms market was already competitive and growing by 2020, with a diverse set of vendors ranging from broad IT management incumbents to focused ML-native startups. The structural drivers — data volume, hybrid cloud complexity, and DevOps acceleration — have only intensified since then. Organizations evaluating AIOps should prioritize integration depth, explainability, and clear ROI metrics alongside raw ML capability. The vendor landscape continues to consolidate through M&A, making platform longevity and vendor financial stability important evaluation criteria.

Original market research attributed to QYResearch, summarized via weeklywall.com (March 2020).

Why AIOps Needs a Structured Deployment Plan

Applying artificial intelligence and machine learning to IT operations — commonly called AIOps — promises faster incident resolution, smarter alert management, and reduced operational noise. The category has produced a wave of specialist vendors and significant enterprise interest. Yet many IT leaders are rightly cautious: an AIOps tool that misfires early can erode both team confidence and executive sponsorship in ways that are hard to recover from.

The antidote is process. Before you license, configure, or run a single model, you need a clear workflow that takes the project from problem definition through to production and continuous refinement. The following seven steps provide that structure.

Step 1: Plan

Resist the temptation to boil the ocean on day one. Choose one or two specific use cases that have measurable pain points and a team that is genuinely open to change. Common starting points include alert noise reduction, automated ticket routing, and anomaly detection on a bounded service or application tier.

During planning, address the following areas:

• Skills inventory: Identify where you have gaps in data science, automation engineering, DevOps, and continuous integration. IT Ops staff need enough working knowledge of machine learning to audit model behaviour once automated control is active — they do not need to become data scientists, but blind trust in a model is risky.
• Workflow impact: Map the operational changes required for your chosen use case. For example, if the AI system is correlating alerts across an e-commerce platform, define what happens when correlated alerts span multiple teams and who owns the escalation path.
• Data readiness: Understand what data the tool requires and whether that data is available natively or needs to be sourced from a CMDB or another system of record. Missing context at ingestion time will degrade model accuracy.
• Goal setting: Define measurable targets — for example, a percentage reduction in alert volume, a reduction in mean time to resolve (MTTR), or a decline in ticket count. Establish how results will be measured and communicated to stakeholders.
• Model training expectations: Document what trainers and operators should expect during the early weeks, when the ML model is still immature and generating lower-confidence outputs. Setting realistic expectations early prevents early abandonment.

Step 2: Socialize

Technical readiness means little if the people affected by the system are not brought along. AIOps triggers legitimate concerns about job displacement, changed responsibilities, and loss of control over operational decisions. Address these concerns directly and early.

Explain how the system works at a conceptual level, what problems it is solving, and how it will change day-to-day workflows. Identify internal evangelists — operators or engineers who understand the technology and are willing to advocate for it — and empower them to answer peer questions and support informal training. Broad organizational buy-in reduces resistance when the system enters production and makes feedback loops faster and more honest.

Step 3: Understand the System’s Capabilities

Before configuration begins, invest time in learning how the chosen AIOps platform actually works, not just what the vendor claims it does. Focus on:

• Data dependencies: Some use cases require additional data to function correctly. Alert correlation, for instance, may need topology or dependency mapping data to validate that a relationship between alerts is genuine rather than coincidental.
• Supported use cases: Confirm which problems the platform addresses out of the box. Common capabilities include anomaly detection, event correlation, alert suppression, and automated ticket routing. Prioritize the use cases most likely to produce quick, visible wins with the lowest operational risk.
• Model transparency: Understand how the platform surfaces its reasoning. Operators who cannot see why a conclusion was reached will struggle to audit or trust the system.

Step 4: Set Up and Observe

Configure the system for your selected use cases and put it into observe-only mode — the system watches live data, applies its algorithms, and generates suggestions, but takes no autonomous action yet. This phase serves two purposes: it validates that the configuration is correct, and it builds operator familiarity with how the system reasons.

A practical signal of solution quality emerges here: if the platform requires weeks of complex configuration before the ML layer can even begin producing outputs, that is worth flagging as a risk. Modern AIOps tooling should reach an observable state relatively quickly. Operators should be able to inspect how algorithms interact with incoming data and trace how specific conclusions were reached — transparency is not a nice-to-have, it is a prerequisite for trustworthy automation.

Step 5: Recommend — Controlled Automation in a Safe Environment

AIOps delivers compounding value when it handles routine, predictable events autonomously. In this step, IT Ops defines a category of well-understood, low-risk events — such as VM utilization threshold breaches or known patch update sequences — and configures the system to apply a standard remediation automatically.

Run this automated handling in a sandbox or against a non-critical workload first. Monitor results closely. The goals at this stage are to verify that automated responses are accurate, that standard procedures are being followed consistently, and that automated handling is not inadvertently masking conditions that would otherwise escalate. Success here is the foundation for expanding automation to higher-stakes workloads.

Step 6: Deploy to Production

Once pilot results are acceptable and stakeholders are comfortable with the recommendations the system is producing, move to a production deployment. Do not treat this as a hard cutover. Run the system in a shadow or test mode alongside production operations for at least two weeks, confirming that outputs remain accurate under real load and that operators find the guidance actionable.

Collect structured feedback from users during this period. Issues that did not surface in the controlled pilot environment will appear under the variety of real-world conditions, and catching them before full autonomy is granted limits operational impact.

Step 7: Review and Refine

After a few weeks of production operation, measure results against the goals established in Step 1. If the target was alert noise reduction, quantify the change. If it was MTTR improvement, compare pre- and post-deployment baselines. Share results transparently with stakeholders.

Supplement quantitative metrics with qualitative feedback. Survey operators about their experience: what is working, what is creating friction, and what they wish the system did differently. Use this input to retrain the model, adjust configurations, or — once the initial use cases are performing well — expand to additional use cases.

AIOps is not a deploy-and-forget technology. The operational environment changes, new alert patterns emerge, and model drift is a real phenomenon. Building review cycles into your operating rhythm ensures the system continues to deliver value rather than quietly degrading.

Summary: The 7-Step AIOps Deployment Framework

AIOps implementations do not need to be high-risk undertakings. Breaking the project into discrete, verifiable stages — each with clear criteria for moving forward — keeps the team in control, builds trust incrementally, and produces a system that operators genuinely rely on rather than work around.

Kubernetes: Powerful, Popular, and Complex

Kubernetes has established itself as the de facto standard for container orchestration in modern cloud architecture. Originally developed at Google under the internal project name Borg, Kubernetes makes it straightforward to deploy and manage containerized applications across hundreds or thousands of machines — without those workloads being physically bound to any particular server.

Compared to first-generation virtualization technologies, this is a significant leap forward. The ability to create, scale, relocate, and terminate entire containerized Linux environments with minimal manual intervention has driven Kubernetes adoption across enterprises of every size. It is increasingly seen as the foundational layer for next-generation applications, from web services to AI model training, and is widely discussed as a modern replacement for Hadoop-era big data infrastructure when combined with S3-compatible object storage.

The Hidden Cost: Operational Complexity

Despite the genuine benefits, Kubernetes is not without serious operational challenges. The very features that make it powerful — ephemeral workloads, dynamic scheduling, rapid horizontal scaling, and deep layering of abstractions — create significant difficulties for operations teams.

Bernard Golden, described at the time of writing as head of cloud strategy at Capital One, captured this tension in a 2018 Medium post:

“While Kubernetes-based applications may be easy to run, Kubernetes itself is no picnic to operate. It is an early stage product which is serving as the foundation of an ecosystem of supplementary products (e.g., Istio) all of which must be installed, configured, and managed correctly to make applications run properly.”

— Bernard Golden, 2018

This complexity is partly a function of Kubernetes’ relative youth at the time, but it is also an inherent consequence of the paradigm shift it represents. Traditional enterprise monitoring approaches — built around static hosts, stable IP addresses, and long-lived processes — do not translate cleanly to an environment where pods are constantly created and destroyed.

Why Legacy Monitoring Tools Fall Short in Kubernetes Environments

Just as VMware’s virtualization layer introduced a new level of abstraction that challenged existing monitoring tools, Kubernetes and the broader microservices revolution create their own monitoring blind spots. Key reasons traditional tools struggle include:

• Ephemeral workloads: Pods spin up and down dynamically. Metrics tied to a specific host or process ID lose meaning quickly.
• Scale: A Kubernetes cluster can generate enormous volumes of telemetry data — metrics, logs, and traces — from hundreds of microservices simultaneously.
• Dynamic networking: Service meshes, ingress controllers, and sidecar proxies add layers of networking complexity that simple ping-based or SNMP-based checks cannot penetrate.
• Multi-layer dependencies: A single user-facing issue might originate in the underlying hardware, the container runtime, the OS kernel, the service mesh, or the application code itself.

Dominic Wellington, described as director of strategic architecture at AIOps vendor Moogsoft, made this point directly in a 2018 blog post:

“Without up-to-date monitoring systems capable of overseeing huge data streams, it will be impossible to maintain the steady uptime and efficiency that enterprise systems require. Even then, your legacy monitoring tools may lack the capability to penetrate and make sense of the data arising within and between containers.”

— Dominic Wellington, Moogsoft, 2018

AIOps: Machine Learning Applied to Kubernetes Monitoring

The answer emerging from this challenge is the application of machine learning to operational data — a category commonly called AIOps. Rather than asking engineers to manually define thresholds and review dashboards across thousands of dynamically changing metrics, AIOps platforms learn what “normal” looks like across different timeframes and automatically surface anomalies that deviate from that baseline.

Anodot, a machine learning software vendor specialising in time-series data analysis, is one example of a company applying this approach to Kubernetes monitoring. According to Yuval Dror, Anodot’s head of DevOps and site reliability engineering (SRE) at the time of the original article, manual troubleshooting at Kubernetes scale is no longer practical:

“When the system is so huge, and you have so many instances that are up, and you dynamically move and add instances on a daily basis, it’s becoming very ridiculous to even try to find issues manually. It doesn’t make sense these days to observe everything, to track all kinds of metrics in the system. It’s getting very hard. It’s almost impossible to find issues using existing tools and dashboards.”

— Yuval Dror, Anodot

How Anomaly Detection Works in Practice

Rather than relying on engineers to watch dashboards, AIOps-based monitoring platforms ingest high-cardinality time-series metrics and use machine learning models to establish dynamic baselines. Here is how that process typically works:

1. Data ingestion: The platform pulls metrics from Kubernetes using mechanisms such as Webhooks, the Kubernetes Metrics API, or Prometheus exporters.
2. Baseline learning: Over time, the model learns what constitutes normal behaviour — including daily, weekly, and seasonal patterns in load and resource consumption.
3. Anomaly detection: When a metric deviates significantly from the learned baseline, the platform flags it as an anomaly, regardless of whether a static threshold would have been breached.
4. Correlation and alerting: Related anomalies are correlated across services and layers to reduce alert noise and surface likely root causes.
5. Automated response (emerging): Some platforms are beginning to build rules-based or ML-driven automated remediation, triggering actions in response to detected conditions without waiting for human intervention.

Anodot’s approach at the time of the original article used Webhooks to collect application data from Kubernetes, with the ML model learning baselines over time and generating alerts when hardware, OS, or application-layer problems were reflected in the tracked metrics. Dror described this correlative approach as the most practical way to detect problems in highly virtualised, rapidly changing environments.

The Business Case for AIOps at Kubernetes Scale

Beyond improved detection rates, there is a straightforward operational and economic argument for AIOps in large Kubernetes deployments:

• Reduced mean time to detect (MTTD): Automated anomaly detection finds issues faster than manual dashboard review, particularly during off-hours.
• Reduced alert fatigue: Correlating related signals reduces the volume of individual alerts that on-call engineers must triage.
• Lower staffing overhead: As Dror noted, ML-driven automation reduces the number of people required to manage large clusters effectively.
• Better uptime: Catching performance degradation before it becomes a user-facing outage directly improves reliability metrics.

“People are moving in this direction because you get better performance, better quality, and all this automation is already built in,” Dror observed. “You don’t have to develop it yourself. Eventually you save a lot of money, because you need less people to manage the cluster.”

What to Look for in a Kubernetes AIOps Solution

If you are evaluating AIOps tools for a Kubernetes environment, consider the following capabilities:

• Native Kubernetes integration: Support for the Kubernetes Metrics API, Prometheus, and common exporters (node-exporter, kube-state-metrics) out of the box.
• High-cardinality time-series support: Kubernetes generates metrics tagged with pod name, namespace, node, deployment, and more. The platform must handle this cardinality without sampling it away.
• Dynamic baselining: Static thresholds are inadequate. Look for platforms that learn and adapt baselines automatically.
• Cross-signal correlation: The ability to connect anomalies across metrics, logs, and traces to reduce noise and accelerate root cause identification.
• Automated remediation hooks: Integration with Kubernetes APIs or CI/CD pipelines to enable automated responses (e.g., scaling, pod restarts) where appropriate.
• Explainability: Alerts should explain why a condition was flagged, not just that it was flagged, to support engineer trust and faster triage.

Looking Ahead

The convergence of Kubernetes adoption and AIOps capability reflects a broader truth about modern infrastructure: as systems become too large and too dynamic for human operators to observe directly, machine learning must take on more of the observability burden. This is not a distant aspiration — it is a practical necessity for any organisation running Kubernetes at meaningful scale today.

Teams that continue to rely solely on static dashboards and manually tuned thresholds will find themselves at an increasing disadvantage as cluster complexity grows. Investing in ML-driven observability alongside Kubernetes adoption is no longer optional for organisations serious about reliability.

What Changed in Moogsoft AIOps 7.2

Moogsoft released version 7.2 of its AIOps platform with several enhancements aimed at reducing alert fatigue, improving workflow automation, and making AI-driven recommendations more transparent for IT operations teams. The update touches three distinct areas: workflow automation, alert clustering visibility, and individual-level performance analytics.

Workflow Engine: Automating Incident Response

The headline addition in version 7.2 is a dedicated Workflow Engine. According to Gabe Coelho-Kostolny, senior product management and product marketing leader at Moogsoft, the engine lets teams trigger automated actions directly from within the platform or in connected external systems. Supported actions include:

• Push notifications to communication and collaboration tools
• Automated ticket creation in ITSM platforms
• Custom remediation tasks that slot into existing incident management workflows

The goal is to close the gap between an AI-generated insight and the operational action that follows—without requiring a human to manually initiate each step.

Expanded Integration Ecosystem

Version 7.2 extends Moogsoft’s integration library with new connectors and proxy support for a broad range of monitoring and collaboration platforms. New additions include:

• New Relic Insights — pulling observability data directly into the AIOps correlation engine
• Microsoft Teams — enabling notifications and workflow triggers within team chat
• Proxy support for polling integrations covering: Zenoss, Zabbix, VMware vCenter, VMware vSphere, SolarWinds, Broadcom Spectrum, and SevOne

The proxy-based polling approach allows Moogsoft to collect data from these platforms in environments where direct agent installation may not be feasible—a practical concern in large enterprise estates with strict network segmentation.

Real-Time Alert Clustering Visualization

One of the persistent criticisms of AIOps platforms is the opacity of their machine learning models—teams are asked to act on recommendations they cannot inspect or tune. Moogsoft addresses this directly in 7.2 by adding tooling that lets IT operations staff:

• Visualize alert clustering algorithms as they run in real time
• Fine-tune clustering parameters when the default behavior does not match operational expectations

Coelho-Kostolny positioned this as a trust-building feature. When operators can see why alerts are being grouped together—and can intervene if the grouping looks wrong—they are more likely to act on the platform’s recommendations rather than second-guessing them.

Individual Statistics Dashboard

The release also ships an analytics dashboard called Individual Statistics. The dashboard is aimed at engineering managers and team leads who need to move beyond team-level aggregate metrics. It provides drill-down views that surface each team member’s workload and key performance indicators—useful for capacity planning, workload balancing, and identifying burnout risk before it becomes a retention problem.

Why DevOps Teams Are the Core AIOps Audience

Coelho-Kostolny noted a strong correlation between organizations that have adopted mature DevOps practices and those actively evaluating or deploying AIOps. The connection is structural rather than coincidental: modern DevOps pipelines—with continuous delivery, microservices, containers, and cloud-native infrastructure—generate an order of magnitude more telemetry events than traditional IT environments. The volume and velocity of those alerts quickly exceeds what human operators can triage manually, creating the conditions where AI-assisted correlation becomes operationally necessary rather than just aspirational.

The concern in these environments is not whether AI will replace IT administrators, but whether IT administrators can keep pace with the rate of change without AI assistance. The practical framing is augmentation: AIOps handles noise reduction and pattern recognition at scale so that human engineers can focus on decisions that actually require judgment.

Data Quality Remains the Hidden Prerequisite

Every AIOps platform depends on large volumes of accurate, well-structured data about the IT environment. Coelho-Kostolny acknowledged that data collection often turns out to be a harder problem than organizations anticipate when they begin an AIOps implementation. Incomplete or inconsistent monitoring coverage upstream will degrade the quality of AI-generated recommendations downstream—no platform can compensate for fundamentally poor input data.

Evaluating AIOps Claims: True AI vs. Rules Engines

With AIOps adoption approaching what Coelho-Kostolny describes as a tipping point in the enterprise, vendor marketing has intensified. He warned buyers to scrutinize what is actually under the hood. A meaningful distinction exists between:

• Genuine machine learning and AI — systems that learn from historical event data, adapt to environmental changes, and surface non-obvious patterns
• Rules-based engines dressed as AI — traditional ITSM correlation rules marketed under an AIOps label, which do not learn and do not generalize beyond what an administrator explicitly programs

The practical test: does the platform’s behavior improve over time with more data and without manual rule updates? If not, it is likely rules-based regardless of the marketing language used.

The Confidence Problem in AI-Driven Operations

Even well-implemented AIOps platforms introduce a risk that is qualitatively different from human error: when an AI system acts on a flawed assumption, it tends to do so consistently and at scale. A single human administrator making a wrong call affects one incident; an AI acting on a bad model can affect every incident simultaneously. This makes explainability and human override capability—exactly what Moogsoft’s clustering visualization feature addresses—essential requirements rather than optional conveniences.

IT teams evaluating AIOps platforms should ask vendors directly: what controls exist to inspect, override, and retrain the model when its recommendations diverge from operational reality?

Summary

For further reading on how AIOps intersects with observability and incident management, see the coverage of AppDynamics’ AIOps approach on devops.com.

Microservices: Legitimate Trend or Hype?

Microservices are not a fad. They represent a specific, well-grounded instance of a broader IT trend toward modularity and component miniaturisation. Where traditional system components were designed to remain stable for years, individual microservices can have lifespans measured in minutes or even seconds — particularly in ephemeral, container-based environments where services are spun up, used, and torn down on demand.

This shift toward smaller, short-lived, independently deployable units aligns with how modern engineering teams want to work: making local changes without needing deep knowledge of every other part of the system, and scaling individual capabilities rather than entire applications.

The Complexity Cost of Modularity

The primary motivation for adopting microservices is agility. DevOps teams can change discrete services to meet evolving business requirements without the risk and effort of modifying a monolithic codebase. From an infrastructure perspective, microservices also give teams more freedom when distributing workloads — for example, choosing which components migrate to the cloud and which remain on-premises, or how to spread services across multiple cloud regions or providers.

But there is no free lunch. Every gain in agility and development speed comes at the cost of increased operational complexity. This is not complexity in a vague sense — it is a measurable increase in system entropy.

In a low-entropy monolithic system, a handful of monitoring vantage points can give you a reliable picture of the overall system state. In a high-entropy microservices environment composed of dozens or hundreds of loosely coupled, independently operating services, you cannot infer the end-to-end state from a few snapshots. You need visibility into nearly every component simultaneously to understand what is actually happening — and that visibility must be continuous, not periodic.

Why Traditional Monitoring Tools Fall Short

Legacy monitoring tools were designed for the low-entropy world of monolithic applications. They work by collecting a manageable number of metrics from a stable set of components, correlating events against known thresholds, and alerting operators when something crosses a boundary.

That model breaks down in a microservices architecture for several reasons:

• Volume: The number of services, instances, and interactions generates orders of magnitude more data than traditional monitoring was designed to handle.
• Dynamism: Services appear and disappear rapidly; static threshold-based alerting cannot keep pace with a constantly shifting topology.
• Interdependencies: A failure in one service may manifest as degraded performance in a completely different part of the stack. Tracing the causal chain manually is impractical.
• Signal-to-noise ratio: Collecting all available data into a centralised big data platform is necessary but not sufficient. As the volume grows, so does the difficulty of identifying meaningful signals amid the noise.

Assembling all your telemetry data into a data lake or SIEM is only the first step. Without a way to find the needle in that haystack, the data is operationally useless.

Where AIOps Fits In

AIOps — the application of AI and machine learning to IT operations — addresses the core problem that high-entropy microservices environments create: pattern detection and root-cause analysis at a scale and speed that exceeds human cognitive capacity.

The fundamental premise is this: abnormal end-to-end system behaviour follows identifiable patterns, but those patterns are embedded in data streams too complex for human operators to parse in real time. An AIOps platform can:

• Ingest high-volume telemetry data from across the entire stack — metrics, logs, traces, and events.
• Identify statistical patterns that deviate from normal baseline behaviour, even when that baseline is itself constantly evolving.
• Correlate events across services to surface probable root causes rather than flooding operators with thousands of individual alerts.
• Present actionable insights that human operators can act on, or feed directly into an orchestration engine for automated remediation.

In short, AIOps does not replace human judgment — it makes human judgment possible again in environments where unassisted human analysis would simply be overwhelmed.

AIOps, Orchestration, and the Cloud

Microservices work best with automated orchestration — platforms such as Kubernetes that schedule, scale, and manage containerised services dynamically. For orchestration to be effective, the decisions driving it need to be grounded in accurate, real-time understanding of system state and business requirements.

This is where AIOps and orchestration intersect most powerfully. An AIOps platform that detects an emerging performance degradation pattern can feed that analysis directly into an orchestration engine — triggering a scale-out, a traffic reroute, or a service restart — before the issue becomes visible to end users.

The introduction of cloud infrastructure adds another layer of difficulty. Extracting telemetry from cloud-managed services can be harder than from on-premises infrastructure, and multi-cloud or hybrid architectures compound this further. However, cloud does not fundamentally change the role AIOps plays. The requirement — full-stack observability and intelligent pattern analysis — remains the same regardless of where the workloads run.

The Role of Healthy Scepticism

Every major technology transition attracts sceptics, and scepticism around microservices is not without merit. The operational burden of a large microservices estate is substantial, and organisations that adopt microservices without accounting for that burden can find themselves with systems that are harder to run than the monoliths they replaced.

In this context, sceptics who push for better observability, root-cause analysis capabilities, and honest total-cost-of-ownership accounting serve a genuinely useful function. If the cost of operating a microservices architecture is not factored in from the beginning, the economic case for the transition will be distorted — and the project risks becoming a technical success but an operational failure.

A related risk: organisations that adopt microservices only for new applications while leaving legacy monoliths untouched often end up with the worst of both worlds — the operational complexity of microservices layered on top of the rigidity of existing systems, without delivering the full modernisation benefits that motivated the change in the first place.

Topology and Graph-Based Analytics: The Next Frontier for AIOps

As AIOps matures alongside microservices adoption, one analytical capability is becoming increasingly central: topological and graph-based analysis.

Understanding causality in a microservices environment requires understanding the relationships between services — which services call which other services, how those relationships change over time, and what the propagation path of a failure looks like as it travels through the dependency graph. This is inherently a graph problem, not a simple time-series or threshold problem.

Some AIOps vendors have invested in graph-based algorithms to model service topology and trace causal paths. However, this is still an area of active development across the industry. As microservices architectures grow in scale and complexity, the ability of an AIOps platform to reason about topology — dynamically, in real time — will increasingly determine how effective that platform is at surfacing actionable root-cause insights.

The expectation is that topological analysis will move from a secondary AIOps capability to a primary one. The particular kind of complexity that microservices introduce — many small components, constantly changing connections, cascading failure modes — is precisely the kind of complexity that graph-based analytics is best suited to address.

Key Takeaways

• Microservices increase system entropy, making traditional monitoring approaches inadequate for understanding end-to-end system state.
• Collecting telemetry data at scale is necessary but not sufficient — AIOps provides the pattern recognition and causal analysis needed to make that data operationally useful.
• AIOps and orchestration engines are complementary: AIOps surfaces the insight; the orchestration engine acts on it.
• Cloud environments complicate telemetry extraction but do not change the fundamental role AIOps plays.
• Operational cost must be accounted for from the start of any microservices initiative — AIOps is part of that cost, and part of what makes the economics viable.
• Graph-based topological analytics is an emerging capability that will become central to AIOps as microservices architectures scale.

DevOps has become one of the defining practices in modern software delivery. At its core, it is about giving developers the autonomy and tooling to move code from source control into a running environment quickly and reliably. But once an application is live — whether in a test environment or production — a broader set of operational concerns takes over: performance monitoring, incident response, log management, and capacity planning.

Teams such as site reliability engineers (SREs) and system administrators handle these concerns, but they are routinely overwhelmed by the sheer volume of data their systems generate. Add to that the additional telemetry flowing from every application moving through a DevOps pipeline, and the data problem becomes acute. This is precisely where AIOps — Artificial Intelligence for IT Operations — acts as a powerful complement to DevOps practices.

What AIOps Does

The primary value of AIOps is breaking down the data silos that naturally form within IT organizations. Different teams — networking, infrastructure, application support, security — each generate their own streams of logs, metrics, and events. Traditionally, that data lives in separate tools and is interpreted only by specialists in each domain.

Before machine learning entered the picture, IT operators typically worked with data in one of three limited ways:

• Targeted lookups: They searched for a specific known error message when a particular business function failed — a workable approach for monolithic applications with well-understood failure modes, but brittle in dynamic environments.
• Domain-scoped monitoring: Each team routinely reviewed only the data from their own area of expertise, making cross-team correlation slow and dependent on manual handoffs.
• Summarized data: When teams did look outside their domain, they usually worked with pre-aggregated data — for example, one data point every five minutes — losing the granularity needed to detect intermittent or subtle problems.

An AIOps platform addresses all three limitations by ingesting high-volume, high-variety operational data from across the environment and applying machine learning to perform continuous trend analysis, anomaly detection, and pattern correlation. Rather than waiting for a human to notice something unusual, the platform surfaces actionable signals through dashboards or feeds them into event management systems for round-the-clock alerting.

As the AIOps platform accumulates history and learns what normal looks like for a given environment, it can also be granted the authority to automatically remediate certain known error conditions — resolving them before they escalate into service-impacting incidents.

How AIOps Helps DevOps Teams Specifically

Machine learning improves DevOps workflows in three concrete ways that map directly onto the three traditional limitations described above.

1. Establishing Baselines in Polyglot Microservice Environments

In a modern microservices architecture, a new service can be conceived, written in any language, and deployed to production without a formal handoff to an operations team. No single person can know every error pattern that might emerge from dozens or hundreds of independently deployed services.

ML-driven AIOps platforms solve this by automatically learning what normal behavior looks like for each new service shortly after it is deployed, then alerting on deviations from that learned baseline. This removes the dependency on pre-written runbooks or tribal knowledge about specific error messages.

2. Cross-Domain Correlation During Incident Response

When a developer is debugging an incident, they often need data from systems outside their immediate area — infrastructure metrics, network logs, database query times — that they have neither the access nor the expertise to interpret efficiently.

AIOps platforms can correlate data across systems using time windows or correlation identifiers (such as a GUID embedded in log entries for distributed tracing) to automatically surface related events from other domains. A developer investigating a slow API response can be shown correlated infrastructure events — for example, a node memory spike or a Kubernetes pod restart — without having to manually query each system.

The same capability works in reverse: if an infrastructure failure occurs, the platform can identify which downstream services — including active build pipelines or running containers — may have been affected.

3. Retaining Full-Fidelity Data Instead of Summaries

Traditional monitoring systems cope with data volume by aggregating metrics into coarse summaries — a single averaged value per polling interval. This means that a transient spike lasting ten seconds in a sixty-second polling window may never be recorded.

Machine learning systems are designed to process large volumes of raw, granular data efficiently. By retaining full-fidelity log and metric streams, AIOps platforms can detect problems that occur frequently but inconsistently — the kind of intermittent failures that summarized data routinely obscures — and surface them for investigation before they become chronic.

Conclusion

AIOps is a relatively young discipline, building on earlier generations of IT operational analytics tools and augmenting them with modern machine learning capabilities. DevOps environments are among the most data-intensive in enterprise IT — generating continuous streams of logs, metrics, traces, and events across tightly coupled but independently deployed services.

Combining AIOps with DevOps practices offers a practical path toward faster incident detection, more efficient root-cause analysis, and ultimately improved service reliability — without requiring teams to hire specialists for every domain or manually correlate data across silos. As both disciplines mature and converge, organizations that invest in this integration are likely to see measurable gains in operational productivity and service quality.