As you know that over the months, what drastic changes are occurring in the field of IT software infrastructure and with which technologically advances. If you have worked with certain sorts of Splunk management tools. After using it, you will adopt a grip like a pro user or advanced user. You know the real facts and figures about the daily delivery of software toolsets with great advancement. How much they’re serving and co-operative with machine-learning data. If you want hands-on phenomenal structured data on a single desk or a single screen. You know the real condition of Splunk like how it works and how it works for the whole IT software management. Doing monitoring, searching, indexing, visualizing, and analyzing from the web server application. They managing the real machine-generated data like “Data for everything values“. Easily you can get analytic generated data with the configuring system.

 If you have gotten all your necessary equipment with lots of source data related to the Splunk user. Let’s talk about the Splunk consultant. The thorough ideology about the consultant gives you all scenario-based research knowledge with an in-depth understanding about deployment methodology and how can you implement on large installations and has deep ability on an expertise-level of Splunk architectures with clustering and scalability data topic. They make you an expert at a certain level of acquiring and managing the data-driven industry. If you meet with it then they create unexpected belief in the work flowing concept of learning and generate monarchy level strength with effective support. So, get every method and gigantic database management control with an advanced learning program at a single mind. If you get introduced to a core consultant then you can acquire certain limits of working experiences with skill practices. So, makes your life busy with lots of server data and data for living conditions.

What are the benefits of Splunk Certified Consultant?

If you get correct brief concepts on how the spunk core consultant makes your life better with lots of equipped information in advance with great job support. Gives the stack learning on Splunk admin, security system, architecture, and phantom. Consultants possess the ability to implementation of all software programs with lots of data performances. With lots of several benefits as much as possible:

• Tailor training to your use cases, workflows and data
• Evaluate workflows
• Don’t look a product in vacuum and evaluate the overall process that they integrate with
• Quantify the value of splunk
• Repurpose your data
• Utilize new ( or little known )features in Splunk
• Prevent data loss

Why we should learn Splunk Certified Consultant?

If you’re looking for the right machine-learning data. So, let’s come to the point of how much it is good and what is the fact & reason behind the becoming of Splunk certified consultant. Mostly, the upcoming technologies data issues are going to engage in the field of multi-tier IT organizations. The learning process about the consultant comes after how much you have self-paced into the machine-generated data. Like you perform on the basis of what you have learnt about Splunk core, admin, security management, and developer. After getting the hands-on challenge ideology, you can act as a core qualified consultant. Then, what is the need for learning? I have few queries in that you can generate a mind-mapping concept on that as insufficient understanding of Splunk architecture can undermine performance, stability, and availability or user may still not understand how to apply the training to their data/ environment. This makes you experts on the basis of what you’re acquiring and implementing in relation to the unstructured large amounts of data. You can easily get command over all the possibilities scenario. On what is data collectively, crawling reports, datasets, and highly configurable alerts with real learning dashboard. This course will produce awareness about the whole IT industry or firm. This sort of highly comprehensive ideology will make eligible to you for any kind of job support role.

How our Splunk Certified Consultant course/training would help?

This training program or courses will make your expertise and develop a highly dense experience about to install the software, how to manage them with great data support, how the Splunk architecture works with multi-tier projects, due to working benefits of a small and large industry of firm. They indulge with lots of working and practical knowledge with some basic concepts of how to monitoring the unscripted data loads, searching for machine-generated data with unlimited sources, indexing the forwarder-based data, and analyzing the real-time data with continuous visibility. This provides you lots of ideas like the installation process, how much implementation in your software system, effectively working on the various dashboard form. How much it is easier to get into your business needs. And how can you ingest software in your entities for real machine learning data? So, if the industries have happened data for everything. This will really help you in the team working process and effectively build your business need with great support.

After getting the working experience on the core consultant tutorial, let’s come to the main topic on where seeks after learning and experiences. After completing any courses and tutorials, we move to the part of certification. It proves how much you’re getting from the course agenda or configuring skillsets. This makes your life appropriate with lots of work flowing ideas and give mental stability with great backhand support. This plays an effective role in career paths like Splunk engineer, spunk architecture, and Splunk administration engineer, etc. If you create high-impact skillful knowledge then you can compete with any IT entities or industry. Regarding this, you will get a high-paid average salary with lots of job demands and opportunities. One more thing is that certification demonstrates the consultant’s ability how to install, implementation of Splunk environment, resize the capacity of software, and maximize the values according to their needs.

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 Today, IT and businesses often find themselves in silos working separately without a shared vision of the customer experience and all the services supporting it. IT must move beyond maintaining up a time to deliver the agility and competitive edge the business needs this means better cohesion between IT and business using predictive tools to prevent service failure meet Splunk IT service intelligence.

 IT service intelligence teams can no longer afford to be reactive especially when it comes to performance and availability. A protective IT strategy is necessary to get ahead of a problem and to protect revenue and your customer experience. Splunk IT service intelligence delivers a predictive IT, running an AIs we have AI scalable data platform that can ingest, search, analyze, and all types of data. ITSI allows you to go from reactive to predictive IT while gaining insights across both IT and business services. Splunk ITSI allows customers to predict service degradation 30 to 40 minutes in advance with the proactive alert for the KPIs most at risk so you can resolve incidents before they start gaining an end-to-end view of both IT and business KPIs on a single screen so you can work on what matters most when it comes to AI ops strategy. ITSI helps you become more proactive and even predictive reduce your incident investigation time business impact outages and event noise ensure your best customer experiences yes and keep the business and IT working better together with Splunk IT service intelligence. Put AI at the heart of your operations and try the free ITSI sandbox from today.

What are the benefits of Splunk IT Service Intelligence?

From the start as we are learning the series of Splunk software tools. In this stack, we discuss that every software has its own performances, capabilities, and security management but how the software works predictive and analytic. This is the reason behind endorsing the system software like how fast service is happening, and how the software is managing by the ITs team. With this effect, there are certain benefits related to Splunk ITSI admin as:-

• Intelligent incident management: Accelerate MTTR using real-time event correlation, automated incident prioritization and integrations with IT service management (ITSM) tools.
• Advance analytics & alerting: It manages a certain function as anomaly detection, adaptive thresholding, and predictive alerts & dashboards.
• Service analyzer: Service monitoring dashboards that display dependent systems and apps. Drill- down into a service’s underlying components to determine what’s impacting its health score.
• End to End visibility: protect business performance by monitoring service health, drill down into underlying infrastructure, all from one dashboard. 

Why we should learn Splunk IT Service Intelligence?

Nowadays, in this trending world. You have met with so many tools in the IT software services. Whenever we talk about Splunk’s advanced services then intelligence plays a prominent role in the IT entities. Now, we are talking about the learning conditions for IT service intelligence. So, what are the reasons to implement a Splunk ITSI? Before we had traditional solutions and those solutions were created as operational challenges like lack of visibility, complex environment, & slow response times. The most important talk is that lack of intelligence service created a significant impact on a business like lost revenue, inefficient productivity, poor customer experiences, & struggle to innovate then it creates technical advance in the field of IT sectors on the basis of Business and IT Service Monitoring, Intelligent Incident Management, Machine Learning, and Predictive Analytics. I wish that these awareness scenarios will give you brain mapping concepts for the Splunk intelligence certified admin.

How our Splunk IT Service Intelligence course/training would help?

This tutorial will help you about understanding the core features of Splunk ITSI like IT event management, service monitoring, and advanced analytics & alerting. It will give you a balancing ideology in the field of KPIs, Data sources, entities, services, incident response, automated remediation, predictive analytics, and event analytics. Through these interactive lectures and hands-on challenges, you will be able to learn to use IT service intelligence, services & glass tables, Notable event & event aggregation policies, and deep dives-investigation with swim lane views. After this, it gives you certification and that makes you qualified for the job opportunities along with the expected high-paid salary and makes your life efficient with future scope.

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Few years back we use to talk and learn about software development lifecycle(SDLC) and the process it goes from requirement → designing → development → testing → deployment → and then maintenance. We were implementing the waterfall model moved to iterative model then to agile model of software development to DevOps.

But, now organisations are trying to integrate AI/ML into their process. This new concept of building ML systems adds/reforms some principles of the SDLC to give rise to a new discipline called MLOps.

MLOps is communication between data scientists and the operations or production team. It’s deeply collaborative in nature, designed to eliminate waste, automate as much as possible, and produce richer, more consistent insights with machine learning.

This concept is deeply collaborative in nature between data scientists and the operations or production team, designed to eradicate waste, automate as much as possible, and produce richer, more consistent insights with machine learning (ml).

Who should learn MLOps?

• DevOps engineers
• Data scientists
• Data engineers
• ML engineers
• Data and Analytics Manager
• Business Analysts
• IT/Software Engineers
• Model Risk Managers/Auditors

What are the prerequisites to learn MLOps?

Basics of DevOps & Machine learning will help.

What are the benefits of MLOps?

• Reduced time to market of AI-driven products
• Enhanced user experience due to the fact that apps get timely updates
• Higher quality of predictions
• The ability of data engineers to focus on building new models — instead of diving into deployment routine.
• Rapid innovation through robust machine learning lifecycle management.
• Creation of reproducible workflows and models.
• Easy deployment of high precision models in any location.
• Effective management of the entire machine learning life cycle.
• Machine Learning Resource Management System and Control.

Why we should learn MLOps?

• Machine learning techniques have finally achieved enough effectiveness and practicality for mass adoption in companies and institutions. MLOps practice is going to be next buzzword of the industry.
• MLOps is still emerging – most companies are in the early stages of understanding and proficiency of this concept. The potential for MLOps professionals in this area, therefore, is enormous.
• ML-based solutions and MLOps become are going to be a crucial for businesses, the trend is for the degree of specialization to increase.
• In the recent four years, the hiring for MLOps roles has grown upto 74% annually.
• MLOps is predicted to grow rapidly in the coming years and is estimated to reach up to $4.5 billion by the end of 2026.
• MLOps being a new field, is witnessing a shortage of skilled professionals.

What is the future of MLOps?

During the few short years MLOPS has grown in popularity, several open source frameworks have emerged. In a move that shows the importance of this practice, as data and technology continue to expand and reach new heights, ML will help organizations of all types develop strong strategies, manage and succeed in the future.

How DevOpsSchool’s MLOps course/Training would help?

DevOpsSchool has a team of passionate instructors, educators, trainers, and mentors. We are highly motivated who build fresh and lasting learning experiences for our participants. Powered by our innovation processes, we provide an environment where learning is easy, constructive, and fruitful.

Reference:- https://www.devopsschool.com/certification/mlops-training-course.html

This MLOps course is a program which tackles the subject of deploying the Machine Learning models in production and at scale. Our MLOps course will help you to learn – best MLOps tools, techniques, and practices for deploying, evaluating, monitoring and operating production ML systems end-to-end. As part of this course, you will learn to deploy models into production environments using cutting edges open-source frameworks.

PLEASANTON, Calif., June 7, 2021 /PRNewswire/ — SpringML, Inc. (“SpringML”), a leader in machine learning and advanced data analytics services, is proud to announce that it has achieved the Security Specialization in Google Cloud Partner Advantage. By earning the Partner Specialization, SpringML has proven their expertise and success in building customer solutions in the Security field using Google Cloud technology.

Partners with this specialization have demonstrated significant success securing customer infrastructure and workflows to Google Cloud. Areas of expertise for SpringML include data encryption management, identifying and masking confidential data, role based access control and network security access to data for internal and external users – all critical components to help lay a secure foundation for data driven transformation.

“We’re pleased that SpringML has achieved the Security Specialization in the Google Cloud Partner Advantage Program,” said Nina Harding, Chief of Global Partner Programs & Strategy at Google Cloud. “Acquiring this specialization is evidence of SpringML’s expertise in building security infrastructure solutions and their continued commitment to helping our customers throughout their digital transformations.”

“SpringML is an agent of change to help enterprises rapidly deploy data-driven digital transformation projects,” said Girish Reddy, CTO of SpringML. “Using Google Cloud tools, we can ensure that security and privacy compliance are maintained at the highest levels throughout a project implementation.”

As enterprises look for new ways to engage with customers, providing creative digital experiences is key. SpringML’s portfolio showcases its ability to deliver data-driven digital transformation projects having built applications for enterprises with an agile, iterative, and incremental delivery model.

Spring ML, Inc.

SpringML delivers data-driven digital transformation outcomes with an experimentation and design thinking mindset. We provide consulting and implementation services and industry-specific analytics solutions that deliver high-impact business value from data. SpringML is a premier Google Cloud partner with capabilities to plan, assess, deploy, and manage data-driven engagements. We have been awarded Google Cloud specializations based on our expertise and customer portfolio for Security, Data Management, Application Development, Data Analytics, Machine Learning, and Marketing Analytics.

DevOps continues to gain traction among organizations as demand grows for digital product and platform development. According to Gartner, 87% of business leaders believe digitalization is a priority. Every DevOps transformation requires a dedicated, continuous learning process and effective implementation to reach maturity. If a practice or pattern is passed over or ignored, it can put a damper on DevOps success. Continuous monitoring, for example, is a very important part of every DevOps life cycle that is often overlooked.

Through effective continuous monitoring efforts, organizations can scale and deliver digital products and platforms faster and more securely. In fact, these continuous monitoring advantages tie directly to modern customer needs. If they haven’t already, business leaders must embrace continuous monitoring along every point of the DevOps life cycle. These best practices can help you get started.

Understanding Continuous Monitoring
The practice of continuous monitoring helps to collect and analyze outcomes, statuses, exceptions and key metrics within each step of the DevOps process – from development to deployment and production. Additionally, it offers deep insight for DevSecOps teams by enabling compliance triggers and security alerts configuration. It allows the organization to detect issues or security concerns throughout every phase of the DevOps lifecycle.

Understanding what continuous monitoring does is a good start. However, to effectively adopt continuous monitoring, the organization must know what to monitor.

What Should Be Monitored
An effective continuous monitoring process collects enough data to gain sufficient understanding of both successes and failures of the organization’s systems and processes. Some of the events collected by the continuous monitoring systems will require immediate attention, while others may lead to longer-term strategy discussions.

Collecting the right information is always tricky considering the number of endpoints that generate logs and events. One should rank the significance of each source in relation to the type of data it produces, the functional area it supports and the impact it has on the system’s performance, as well as on organization’s goals and commitments (requirements posed by SLA’s, regulations, contracts, etc.).

A combination of technology and strategy helps ensure that the right data is collected at the right time. The next step, and perhaps one of the most significant challenges, is finding the balance between monitoring and analysis.

How to Achieve Balance
Balance is, by far, one of the most difficult decisions in log monitoring and analysis. Gather too little data and risk missing early trouble indicators or important alerts. Collect too much and get buried in analysis paralysis – essentially, leading to the same situation of overlooking items of significance. Striking the right balance requires understanding the system’s continuous monitoring processing capacity and using its full potential. This may also involve data preparation steps during intake to ensure optimal processing and storage of the collected material.

Offloading mundane tasks and parts of the analysis to machine learning and artificial intelligence (ML and AI) and fuzzy logic systems will enable big data processing gains. With this approach, the continuous monitoring capacity can be significantly increased, along with the semantic quality of the alerts and notifications produced by the system. As a result, security operation center (SOC) staff can devote their attention to the analysis of events which are more meaningful to the system and operations, leading to a more efficient continuous monitoring process.

A DevOps transformation will likely not reach full maturity unless the organization applies continuous monitoring to the DevOps life cycle. Although often overlooked, it remains central to DevOps success. With this in mind, a basic understanding of continuous monitoring, what to monitor and finding the balance between log monitoring and analysis, serve as a foundation for getting started with continuous monitoring.

HOLMDEL, N.J., March 10, 2021 /PRNewswire/ — Vonage (Nasdaq: VG), a global leader in cloud communications helping businesses accelerate their digital transformation, has announced that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status in the new Applied Artificial Intelligence (Applied AI) category.

This designation recognizes that Vonage has demonstrated deep experience and expertise in building or integrating ML solutions on AWS. AWS Partners recognized as part of the AWS Machine Learning Competency expansion help customers take advantage of intelligent solutions for the business, from creating, automating, and managing end-to-end ML workflows to modernizing applications with machine intelligence.

By working with AWS, Vonage is bringing AI into the contact center through AWS Contact Center Intelligence (CCI), with tools for Speech Recognition, Natural Language processing, Machine Learning and Text-to-Speech to deliver personalized, flexible and integrated customer experiences. With Vonage for AWS Contact Center Intelligence, Vonage is delivering the power of AWS cloud native services for AI and Machine Learning through the Vonage API Platform, enabling brands to access the capabilities of AWS AI and ML engines within any existing contact center environment.

“Now more than ever, businesses are looking to transform themselves with the latest ML and AI solutions on AWS. To help customers make the right choices when finding the most qualified AWS Partner for the job, we’ve expanded the AWS Machine Learning Competency to help customers easily identify expert AWS Partners in the areas of Applied AI and ML Ops,” said Julien Simon, Global AI & ML Evangelist, AWS. “These AWS Partners are vetted by AWS for their technology capabilities and have a solid track record of success, and we are delighted to include Vonage in the launch of the new AWS Machine Learning Competency Categories. We look forward to innovating together and continuing to serve our customers with innovative and validated AWS Partner technology and consulting offerings.”

“Vonage is thrilled to be among the first AWS Partners to achieve AWS Machine Learning Competency status in Applied AI,” said Jay Bellissimo, Vonage Chief Operating Officer. “Not only does this certification underscore our long and successful history of working closely with AWS but it reinforces Vonage’s continued investment in the delivery of AI and machine learning solutions that are flexible, intelligent and personal, empowering our customers to do what is next and stay ahead.”

Key features that this partnership brings to the Vonage for AWS CCI offering include:

Customer Self Service – Customers can select self resolution for simpler tasks, such as the status of a bill, updating information or getting a quick answer to frequently asked questions.
Live Agent Assist Tools – Ability to connect with a live agent, when needed, with tools for agent prompts and real-time analytics to provide a seamless and contextual customer experience.
Post-Call Analytics – Collected data enables contact center agents to dive deeper into the delivery of customer experiences and leverage automatic analyses as feedback to improve future interactions.
AWS is enabling scalable, flexible, and cost-effective solutions from startups to global enterprises. To support the seamless integration and deployment of these solutions, AWS established the AWS Competency Program to help customers identify Consulting and Technology AWS Partners with deep industry experience and expertise.

AI and ML driven applications are maturing rapidly and creating new demands on enterprises. AWS is keeping pace and continuously evolving AWS Competency Programs to provide customers an ability to engage enhanced AWS Partner technology and consulting offerings. AWS launched the new Categories within the AWS Machine Learning Competency to help customers easily and confidently identify and engage highly specialized AWS Partners with Applied AI. With this program expansion, customers will be able to go beyond the current data processing and data science platform capabilities and find experienced AWS Partners who will help find off-the-shelf packages for their business problems.

Vonage

Vonage, (Nasdaq:VG) a global cloud communications leader, helps businesses accelerate their digital transformation. Vonage’s Communications Platform is fully programmable and allows for the integration of Video, Voice, Chat, Messaging and Verification into existing products, workflows and systems. Vonage’s fully programmable unified communications and contact center applications are built from the Vonage platform and enable companies to transform how they communicate and operate from the office or anywhere, providing enormous flexibility and ensuring business continuity.

Imagine this familiar scenario: A new incident hits production; your team is getting paged and a growing number of people are getting on the conference bridge or joining the Slack channel to troubleshoot. A lot of chatter takes place. Everyone is trying to figure out what went wrong.

But almost immediately, it feels like there are a lot more people on this than are necessary. If I had to throw a number out, maybe 50% more people than necessary. There’s way too much noise and not enough clear information about the root cause of the incident. Getting so many people involved is not exactly efficient and inflates the time required to resolve this incident.

As an incident first responder, my top priority is always to understand what went wrong and where, so I try to involve the right people. At the beginning, it’s not always easy to immediately answer these questions, so we bring in more people thinking we’ll collaborate and mitigate the incident quickly. But it never happens that way. In the long run, it ends up decreasing the team’s productivity and effectiveness.

In this blog, I’ll tell you how to address incidents and resolve production issues faster with a feature in our Applied Intelligence product, and I’ll share some of the details about how it works. Within the Incident Intelligence capability is a feature called suggested responders, and it augments every new incident by identifying in real time the most relevant team members to help resolve it. Applying machine learning (ML) to analyze your past interactions, New Relic identifies the most relevant users in your organization who can help resolve an incident.

If you’re an Incident Intelligence customer, suggested responders is automatically available with no configuration or setup needed.

Machine learning powered

When you opt in to Incident Intelligence, New Relic automatically trains an ML model to analyze your account’s historic incident data and overall New Relic user interaction. As soon as the model training is completed-which happens as more and more users interact with the system-future incidents will be augmented in real time with those people best suited to help resolve them. The responders list is displayed both in the New Relic issue page and within the issue notification payload, so you can see it directly from where you usually respond to incidents.

For each suggestion, you can provide feedback in the form of a thumbs up/down vote, so that the model could become even more accurate.

How the machine learning model works

The suggested responders model uses past violations, combined with user analytics data, to predict the most likely responders for new violations. The model consists of three phases: supervisedpattern recognition,label spreading, and a recommender engine.

Supervised pattern recognition: This step predicts, based on how users interacted with the New Relic platform in the past, whether they closed the violation. It curates a labeled dataset that includes only violations for which we know for certain the user who closed them. We then train a binary classifier that treats the users’ actions as features and whether they closed the violations as the target. Suggested responders uses this trained model in the next step.

Label spreading: The purpose of this step is to increase the coverage of our labeled dataset by augmenting it with violations for which we do not know the user who closed it. We construct an unlabeled dataset that links each violation to the user actions that occurred while the violations were still open. We then run the unlabeled dataset through the trained model. The output of this stage is a responders table that assigns, for each violation-user pair, the likelihood that the user closed the violation. This table is used in the next step to identify the users who are most likely to resolve a certain violation. We update the table periodically, so that it will reflect the most recent violations.

Recommender engine: The third and last step of the model uses the responders table generated in the previous stage to suggest responders to new violations. When a new violation occurs, the model computes a similarity score between that violation and the violations that appear in the responders table. The similarity score is based on many fields, such as the type of product, target id, policy id, condition id, golden signal, and components of the violation at hand. We then use the similarity score as a weight to compute a weighted score for each user across all the violations in the table. The weighted score can be interpreted as the (predicted) level of involvement a user had with similar violations in the past. The model returns the users who received the highest weighted score. These are the suggested responders.

How to use suggested responders to route incident notifications

Now that you know how suggested responders works, you can start using it in new ways and further increase your response efficiency and resolve production issues faster. One way to do that is by configuring a pathway logic that, for instance, routes the alerts notification to a specific channel whenever it predicts certain users as suggested responders for a given incident. This lets you ensure that the right people will always be notified for the right incidents.

Get started now and resolve production issues faster

To get up and running with suggested responders, all you need to do is to opt in to Incident Intelligence and start ingesting your violations. The model trains itself and gives you suggestions about 24 hours after opt-in. The more you engage, the more recommendations will increase. For instance, if you interact with New Relic One entities or define more alert conditions and policies in your account, the model will increase recommendations.

The views expressed on this blog are those of the author and do not necessarily reflect the views of New Relic. Any solutions offered by the author are environment-specific and not part of the commercial solutions or support offered by New Relic. Please join us exclusively at the Explorer’s Hub (discuss.newrelic.com) for questions and support related to this blog post. This blog may contain links to content on third-party sites. By providing such links, New Relic does not adopt, guarantee, approve or endorse the information, views or products available on such sites.

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.

Kubernetes is quickly consolidating its place as the leading container orchestration platform for cloud-native applications, with adoption at 59% among enterprise IT professionals as of March. But while Kubernetes delivers agility, flexibility and scalability for DevOps teams, it also creates complexity that can be an enigma for SecOps teams—especially when something goes wrong.

When it comes to detecting threats and tracking down breaches in Kubernetes, security teams’ key asset is the Kubernetes API server audit log. The audit log captures every successful or unsuccessful API server call, whether by human users, automation pipelines, controllers, operators, system components and, in general, API server operations performed by principals identified as service accounts. In other words, the audit log is the single source of truth for what’s happening inside a Kubernetes infrastructure.

But just because the audit log tracks all the activity in your Kubernetes environment doesn’t make it easy to find what you’re looking for. The larger your infrastructure, the more events there will be to parse through. The Kubernetes audit log is also extremely verbose, capturing a number of identifying attributes for each action, such as detailed information about what resource was accessed, who accessed it or what IP address the request came from. When the Kubernetes infrastructure at hand involves multiple clusters, which is the most common use pattern, it becomes a daunting task to place effective security monitoring controls across all clusters and manual security audit log monitoring becomes extremely impractical.

One simple approach to automate Kubernetes Audit log monitoring, which is quite useful for compliance purposes, is through the use of simple static policies that trigger alerts when a violation occurs. For example, an alert could be triggered if a human user accesses a workload that processes sensitive data. This simplified approach is useful when you have a precise notion, as in cases of regulatory compliance of what use patterns are allowed. However, in real-life deployments, adversaries never limit themselves to what is allowed.

Another approach is threshold-based security monitoring, which captures when API events, or combinations of events, rise above a certain threshold. However, this tactic can be easily bypassed by an adversary simply by controlling the rate of compromise attempts, spreading the attack out over time to avoid crossing the threshold.

Threshold-based based monitoring is also prone to false positives that can really wear out security and teams and create alert fatigue. For example, periodic batch processing workloads, which create a frenzy of API calls, would break through pre-configured thresholds but aren’t actually a threat. To separate threats from routine deployment events, you need to understand the context, something threshold-based monitoring alone cannot provide.

With significant, high-maintenance efforts, and potentially high rates of false alerts, threshold-based monitoring can help security or operations teams to catch known threats and vulnerabilities, but it won’t catch unknown threats or vulnerabilities. Again, to identify a threat, you need to be able to understand the context of any anomalous behavior: who is making the API calls, from where and what resources are they accessing. You also need to observe this data over time to establish what normal behavior looks like so you can differentiate it from anomalous behavior.

As a powerful, elastic and flexible cloud-native application infrastructure that drives many automatic processes, Kubernetes is inherently complex. To detect a security threat in Kubernetes, you need an adaptive security monitoring solution powered by machine learning. Through time-based observation of the various actors within a Kubernetes cluster that leverage the API server audit log, machine learning can learn and detect anomalous patterns in actor activity that simple tools will miss.

Learning the behavior of your clusters is no trivial task. Profiles for different users, components and automated services need to be built over time, and with so many moving parts, this is impractical to do manually. At the same time, new vulnerabilities are constantly coming to light, and there will always be vulnerabilities that are waiting to be uncovered. You can set alerts for vulnerabilities you know, but to catch unknown threats you need a strong baseline of activity in your cluster to monitor against, something that the right machine learning algorithms can readily capture and adapt to over time.

While machine learning is a broad topic, it is quite clear that the traditional approach of supervised or unsupervised offline model creations would not yield satisfying detection results, simply because different environments are accessed and behave differently. There has to be a component that learns and adapts to the specific Kubernetes environment being monitored.

Here are two security use cases that machine learning can reveal through the analysis of the Kubernetes audit log stream:

Stolen Service Account Tokens and User Credentials

Machine learning-based audit log monitoring tools can alert security teams of suspected credential theft. For example, if the same cluster credentials are reused from multiple different geographic or network locations within a short period of time, it can be flagged as an anomalous behavior and, if combined with additional abnormalities, could indicate stolen credentials.

By tracking and learning the geographical attributes and the access patterns of individual users or principals, machine learning can detect this anomalous behavior and flag it for security teams. In response, the security team can take actions to reduce or eliminate the blast radius—such as limiting access to the API server from the specific suspected IP address or modifying the Kubernetes role-based access control (RBAC) policy to reduce access privileges for the specific account.

Misconfigured Kubernetes RBAC

In addition to detecting threat actors that attempt to pivot within the Kubernetes cluster, machine learning can also leverage the audit log to detect attempted exploits of known or unknown vulnerabilities if the RBAC permissions are misconfigured or over-permissive. On the flip side, once RBAC policies are properly configured, reducing RBAC privileges to the required minimum, machine learning can alert when unauthorized users make unsuccessful API calls to access sensitive resources, indicating a failed exploit attempt.

Ultimately, the audit log is an incredibly rich source of information on activity within your Kubernetes environment and a valuable tool for both detecting threats and vulnerabilities and forensically tracing breaches. Although the complexity prohibits manual monitoring, machine learning can give security teams real-time observability into security incidents within their Kubernetes environments and enable informed investigations and audits that get to the root of problems more quickly.

Scaling test automation and managing it over time remains a challenge for DevOps teams. Development teams can utilize machine learning (ML) both in the platform’s test automation authoring and execution phases, as well as in the post-execution test analysis that includes looking at trends, patterns and impact on the business.

Before diving deeper into how ML can help during both of these phases of the test automation process, it is important to understand the root causes of why test automation is so unstable when not utilizing ML technologies:

The testing stability of both mobile and web apps are often impacted by elements within them that are either dynamic by definition (e.g., react native apps), or that were changed by the developers.
Testing stability can also be impacted when changes are made to the data that the test is dependent on, or more commonly, changes are made directly to the app (i.e. new screens, buttons, user flows or user inputs are added).
Non-ML test scripts are static, so they cannot automatically adapt and overcome the above changes. This inability to adapt results in test failures, flaky/brittle tests, build failures, inconsistent test data and more.
Let’s dig into a few specific ways that machine learning can be valuable for DevOps teams:

Make sense of extremely high quantities of test data
Organizations that implement continuous testing within Agile and DevOps execute a large variety of testing types multiple times a day. This includes unit, API, functional, accessibility, integration and other testing types.

With each test execution, the amount of test data that’s being created grows significantly, making the decision-making process harder. From understanding where the key issues in the product are, through visualizing the most unstable test cases and other areas to focus on, ML in test reporting and analysis makes life easier for executives.

With AI/ML systems, executives should be able to better slice and dice test data, understand trends and patterns, quantify business risks, and make decisions faster and continuously. For example, learning which CI jobs are more valuable or lengthy, or which platforms under test (mobile, web, desktop) are faultier than others.

Without the help of AI or machine learning, the work is error prone, manual and sometimes impossible. With AI/ML, practitioners of test data analysis have the opportunity to add features around:

Test impact analysis
Security holes
Platform-specific defects
Test environment instabilities
Recurring patterns in test failures
Application element locators’ brittleness
Make actionable decisions around quality for specific releases
With DevOps, feature teams or squads are delivering new pieces of code and value to customers almost on a daily basis. Understanding the level of quality, usability and other aspects of code quality on each feature is a huge benefit to the developers.

By utilizing AI/ML to automatically scan the new code, analyze security issues and identify test coverage gaps, teams can advance their maturity and deliver better code faster. As an example, code-climate can review any code changes upon a pull request and spot quality issues, and optimize the entire pipeline. In addition, many DevOps teams today leverage the feature flags technique to gradually expose new features, and hide them in cases of issues.

Enhance test stability over time through self-healing and other test impact analysis (TIA) abilities
In traditional test automation projects, the test engineers often struggle to continuously maintain the scripts each time a new build is being delivered for testing, or new functionality is added to the app under test.

In most cases, these events break the test automation scripts — either due to a new element ID that was introduced or changed since the previous app, or a new platform-specific capability or popup was added that interferes with the test execution flow. In the mobile landscape specifically, new OS versions typically change the UI and add new alerts or security popups on top of the app. These kinds of unexpected events would break a standard test automation script.

With AI/ML and self-healing abilities, a test automation framework can automatically identify the change made to an element locator (ID), or a screen/flow that was added between predefined test automation steps, and either quickly fix them on the fly, or alert and suggest the quick fix to the developers. Obviously, with such capabilities, test scripts that are embedded into CI/CD schedulers will run much smoother and require less intervention by developers.

An additional benefit would also be the reduction of “noise” within the pipeline. Most of the above mentioned brittleness in testing are not real defects, but interruptions to automation scripts. By eliminating them proactively through AI, teams will get more time back to focus on real issues.

Conclusion
When thinking about ML within the DevOps pipeline, it is also critical to consider how ML is able to analyze and monitor ongoing CI builds, and point out trends within build-acceptance testing, unit or API testing, and other testing areas. An ML algorithm can look into the entire CI pipeline and highlight builds that are consistently broken, lengthy or inefficient. In today’s reality, CI builds are often flaky, repeatedly failing without proper attention. With ML entering this process, the immediate value is a shorter cycle and more stable builds, which translates into faster feedback to developers and cost savings to the business

Overview

Amazon Web Services (AWS) joined a multi-organization effort alongside military social network RallyPoint, the U.S. Department of Veterans Affairs (VA), and Harvard University to develop a machine learning model designed to detect signs of mental health risk among veterans. The initiative was reported by Nextgov and represents a practical application of cloud-based ML tooling to a high-stakes public health challenge.

The Partnership and Its Goals

The collaboration brought together four distinct stakeholders, each contributing domain expertise:

• AWS / Amazon Machine Learning Solutions Lab – Provided cloud infrastructure, data-labeling services, and the Amazon SageMaker managed ML platform.
• RallyPoint – A military-focused social network whose anonymous public posts served as the data source for model training.
• Department of Veterans Affairs (VA) – The anchor government partner, operating under a memorandum of understanding with RallyPoint signed in 2018 to improve veteran community interactions.
• Harvard University’s Nock Lab – Mental health professionals who collaborated on identifying meaningful behavioral signals and defining high-value use cases for the model.

The stated aim is to use data science to surface early indicators of mental health risk in veteran-generated content, then connect at-risk users to appropriate resources such as mental health programs, peer support groups, and crisis hotlines.

How AWS SageMaker Was Used

AWS contributed two core technical capabilities to the project:

1. Data labeling – Annotating training data so the model could learn to associate specific linguistic or behavioral patterns with mental health risk indicators, as defined by clinical professionals at the Nock Lab.
2. Amazon SageMaker – AWS’s fully managed ML platform was used to build, train, and deploy the detection model. SageMaker removes much of the infrastructure overhead from the ML development cycle, allowing the team to focus on model quality and clinical relevance rather than server management.

The model was trained on anonymous public posts from the RallyPoint platform, meaning no personally identifiable information was required to generate risk signals—an important privacy consideration when working with a sensitive population.

Alignment with Veteran Suicide Prevention Policy

RallyPoint CEO Dave Gowel framed the effort as a direct contribution to government-level initiatives including the VA’s Suicide Prevention Program and the President’s Roadmap to Empower Veterans and End a National Tragedy of Suicide (PREVENTS). Veteran suicide is a recognized public health crisis in the United States, and the ability to detect distress signals at scale—before a crisis escalates—could complement existing outreach efforts.

The team indicated plans to continue refining the model and actively sought feedback from stakeholders in the months following the initial announcement.

Why This Matters for ML Practitioners

From a technical standpoint, this project illustrates several patterns relevant to ML engineers and DevOps teams building similar systems:

• Domain expert annotation is critical. Mental health risk classification is not a task that can be crowdsourced without clinical oversight. Partnering with subject matter experts (in this case, Harvard’s Nock Lab) to define and label training data is essential for model validity.
• Managed ML services lower the barrier to entry. SageMaker’s managed infrastructure allowed a research-oriented team to move from raw data to a trained model without needing to stand up and maintain dedicated ML infrastructure.
• Privacy by design. Using anonymous public posts rather than private messages or user profiles reduces privacy risk, though it also introduces limitations on signal richness.
• Cross-sector data agreements require groundwork. The 2018 MOU between RallyPoint and the VA provided the legal and operational foundation that made this 2020 technical collaboration possible—a reminder that data access in sensitive domains must be negotiated long before model training begins.

Video

For veteran mental health support, contact the Veterans Crisis Line: call 1-800-273-8255 and press 1, text 838255, or chat at VeteransCrisisLine.net.

Infoworks DataFoundry Now Available as a Free Trial on Google Cloud Marketplace

Infoworks.io has announced a free trial program for Infoworks DataFoundry on the Google Cloud Marketplace. The trial introduces an updated version of DataFoundry featuring an interactive assistant designed to guide users through data onboarding to Google Cloud and the development of advanced analytics use cases.

DataFoundry had previously been made available on the Google Cloud Marketplace the prior fall. This free trial expands access and lowers the barrier to entry for Google Cloud customers looking to automate data operations at scale.

What Is Infoworks DataFoundry?

DataFoundry is Infoworks’ flagship platform for Enterprise Data Operations and Orchestration (EDO2). It is designed to automate and accelerate the deployment and orchestration of analytics projects, removing the need for manual coding or deep infrastructure management. Key characteristics include:

• No-code automation: Data engineers, scientists, analysts, and business users can build data pipelines without writing custom code.
• Scalable data onboarding: Automates ingestion and preparation of enterprise data at scale.
• Multi-environment support: Works across cloud, hybrid, multi-cloud, and on-premises environments.
• Rapid time-to-value: Enables organizations to deploy analytics projects to production within days rather than months.

What the Free Trial Includes

The DataFoundry free trial runs natively on Google Cloud and is optimized to leverage Google Cloud’s underlying infrastructure. The trial package includes:

• An interactive assistant providing step-by-step guidance for building data onboarding and analytics use cases.
• Integration with Google Cloud’s advanced analytics services, including BigQuery and TensorFlow.
• Support for third-party AI and machine learning solutions available on Google Cloud.
• Access to DataFoundry’s full pipeline orchestration and automation capabilities.

Why This Matters for Data Engineering Teams

One of the persistent challenges for enterprises migrating to or expanding on cloud platforms is the complexity of data operations — ingesting, transforming, governing, and operationalizing data at scale. This work typically consumes significant data engineering time and specialized talent.

Russ Barck, Vice President of Strategic Alliances at Infoworks, framed the value proposition directly:

“Infoworks DataFoundry frees data engineers, scientists, analysts and even business users to focus on leveraging Google Cloud in delivering use cases that drive digital transformation. Our unique automated, no-code offering means the major time and talent sink of dealing with underlying data operations and orchestration complexity is no longer an issue.”

— Russ Barck, VP Strategic Alliances, Infoworks

Manvinder Singh, Director of Partnerships at Google Cloud, also commented on the partnership:

“Businesses today need to derive more insights and greater value from their data. We’re delighted that Infoworks will make its enhanced DataFoundry capabilities available via Google Cloud Marketplace, helping customers more seamlessly bring their data to Google Cloud.”

— Manvinder Singh, Director of Partnerships, Google Cloud

Supported Industries and Customer Base

According to Infoworks, DataFoundry is already deployed by some of the world’s largest enterprises across a range of industries, including:

• Financial services
• Retail
• Technology
• Healthcare
• Oil and gas
• Manufacturing

How to Access the Free Trial

The DataFoundry free trial can be accessed directly through the Google Cloud Marketplace or via the Infoworks website at infoworks.io.

About Infoworks

Infoworks offers an Enterprise Data Operations and Orchestration (EDO2) platform that automates the full lifecycle of analytics project deployment — from data ingestion and preparation through to production operationalization. The platform supports cloud, hybrid, multi-cloud, and on-premises deployments. More information is available at infoworks.io.

Why Enterprises Are Turning to the Cloud for Machine Learning

Machine learning, a branch of artificial intelligence, encompasses techniques that range from classical statistical models to deep-learning architectures that loosely emulate how the brain processes information. Enterprises are finding value in ML across a growing number of use cases:

• Customer service automation: Conversational AI and chatbots handle routine support interactions, reducing agent load and improving response times.
• Dynamic and surge pricing: ML models ingest signals such as demand, inventory, and customer history to adjust pricing in real time—common in e-commerce, ride-sharing, and hospitality.
• Churn prediction: Telecom and subscription businesses use classification models to identify customers likely to leave, enabling targeted retention campaigns before churn occurs.
• Internal cost reduction: Predictive maintenance, demand forecasting, and intelligent process automation all cut operational overhead.

Cloud adoption has accelerated these deployments significantly. Provisioning GPU or purpose-built ML accelerator capacity on demand—rather than owning it—makes experimentation economically viable even for mid-sized organizations. The three hyperscalers have each invested heavily in ML-specific tooling designed to reduce the time from raw data to a production model.

AWS: Amazon SageMaker and Custom Silicon

Amazon Web Services offers a broad ML portfolio anchored by Amazon SageMaker, a fully managed service that covers the entire model lifecycle: data labeling, feature engineering, model training, hyperparameter tuning, evaluation, and deployment. SageMaker Studio provides a web-based IDE that lets data scientists run notebooks, visualize experiments, and compare model versions in one place—reducing the context-switching that slows ML projects.

Beyond general-purpose GPU instances, AWS has developed AWS Inferentia, a custom inference chip designed to deliver high throughput at lower cost for production workloads such as search ranking, recommendation engines, dynamic pricing, and automated customer support. Inferentia-powered instances are available through the cloud without any hardware procurement on the customer’s side.

Other notable AWS ML services include Rekognition (computer vision), Comprehend (natural language processing), Forecast (time-series forecasting), and Fraud Detector—each a managed API that packages pre-built model architectures for common problem types.

Google Cloud: AI Platform and the TensorFlow Ecosystem

Google’s ML credentials are well established. The company open-sourced TensorFlow, now one of the most widely used deep-learning frameworks, and is associated with DeepMind, the research lab behind AlphaGo and AlphaFold. That research culture permeates Google Cloud’s enterprise offerings.

Google Cloud AI Platform (now part of Vertex AI) integrates the ML pipeline end to end: managed data storage and labeling through Vertex Data Labeling, distributed training on TPUs or GPUs, experiment tracking, model registry, and one-click deployment to scalable prediction endpoints. Vertex AI Pipelines allows teams to define reproducible, containerized ML workflows using Kubeflow Pipelines or TFX.

Google also provides AutoML products that let teams with limited ML expertise train high-quality models on structured data, images, text, and video by specifying a dataset and a target metric—the platform handles architecture search and hyperparameter tuning automatically.

Microsoft Azure: MLOps and Responsible AI

Azure Machine Learning is Microsoft’s managed ML platform, and it explicitly positions itself around MLOps—the discipline of applying DevOps principles (version control, CI/CD, monitoring, reproducibility) to the machine learning lifecycle. For DevOps teams already working within the Azure ecosystem, this is a meaningful integration point: Azure ML pipelines can be triggered from Azure DevOps or GitHub Actions, and model registries integrate with existing release workflows.

The platform accommodates multiple skill levels. Engineers comfortable with Python SDKs can script every pipeline stage; data scientists who prefer a visual approach can use Azure ML Designer, a drag-and-drop canvas for building training pipelines without writing code.

A notable differentiator is Azure’s emphasis on Responsible AI. The platform includes tools for model interpretability (understanding why a model makes a particular prediction), fairness assessment (detecting and mitigating bias across demographic groups), and differential privacy. For enterprises operating in regulated industries or serving diverse customer populations, these guardrails are increasingly required—not optional.

Choosing the Right Cloud ML Platform

No single platform is universally superior. Selection typically depends on existing cloud commitments, the skill set of your data-science and engineering teams, and the specific ML tasks at hand. A few practical considerations:

• If you are already on AWS: SageMaker’s tight integration with S3, IAM, and other AWS services reduces friction. Inferentia instances are worth evaluating for high-volume inference workloads.
• If your team uses TensorFlow or JAX: Google Cloud’s native TPU support and Vertex AI’s TFX integration may offer the smoothest experience.
• If your organization runs on Azure DevOps or Microsoft 365: Azure ML’s MLOps integrations and Active Directory support simplify governance and access control.
• For teams new to ML: AutoML offerings across all three platforms lower the entry point—start there and move to custom training as requirements grow.

The Road Ahead for Enterprise ML

Managed cloud ML services have substantially reduced the engineering overhead of getting a model into production. The remaining challenges—data quality, model governance, organizational change management, and the interpretability of complex models—are where most enterprise ML projects still stall. Platforms are investing in tooling to address these gaps, but they require deliberate process changes inside organizations as much as they require technology.

For DevOps and platform engineering teams, the practical implication is clear: ML workloads are becoming a standard part of the production software estate. Understanding the deployment, monitoring, and rollback patterns for ML models—and integrating them into existing CI/CD and observability stacks—is increasingly a core competency, not a specialist concern.

This article is an independent technical overview. The original source material was attributed to analyticsinsight.net (August 2020).

This article examines ten practical ways AI is accelerating DevOps today, drawing on published industry data and practitioner perspectives.

Industry Context

Several market signals underscore why AI adoption in DevOps is accelerating:

• Startups offering AI-powered software development tools raised $704 million over the twelve months ending September 2019, according to a Deloitte report published in 2020.
• IDC forecast the global market for custom application development services to grow from $47 billion in 2018 to more than $61 billion in 2023, a 5.3% CAGR.
• Poor software quality cost US organizations an estimated $319 billion in 2019, according to the Consortium for IT Software Quality.
• Gartner predicted that 40% of DevOps teams would be using application and infrastructure monitoring tools with integrated AIOps capabilities by 2023.

These numbers frame a clear business case: AI investment in DevOps is driven by the twin pressures of accelerating delivery speed and reducing the cost of defects.

1. AI-Powered Code Completion and Autosuggestion

One of the highest-impact AI applications in DevOps is real-time code autosuggestion. Tools trained on large code corpora use supervised machine learning to predict the next logical code segment as a developer types, reducing keystrokes, lookup time, and context switching.

Early implementations were inconsistent, but improvements in supervised ML models—particularly around how quickly models adapt to a team’s specific codebase and style—have made suggestions significantly more accurate. Microsoft’s Visual Studio IntelliCode is a widely adopted example in this category. DevOps practitioners in enterprise CRM, supply chain, and social media companies have cited AI code autosuggestion as the single most productivity-positive AI capability they have adopted.

2. Streamlining Requirements Management

Managing requirements documents is one of the most time-consuming non-coding tasks DevOps teams face. Requirements go through multiple iterations, involve stakeholders with varying levels of technical literacy, and must remain tightly aligned with the evolving codebase.

AI—and specifically Natural Language Processing (NLP)—is being applied to assist with drafting, editing, validating, and cross-referencing requirements. NLP models can flag ambiguous language, identify contradictions between requirements, and suggest standardized phrasing. Teams adopting AI-based requirements management tools have reported requirements review time reductions of more than 50%, allowing engineers to redirect effort toward coding and design. Atlassian’s Jira Software is widely used as a foundation for structured requirements workflows in enterprise DevOps environments.

3. AI-Assisted Bug Detection and Code Remediation

Static analysis has long been part of code review pipelines, but AI is taking defect detection further by predicting where bugs are likely to occur and suggesting remediation steps before a human reviewer encounters the issue.

Facebook has publicly described a bug detection system that predicts defects and suggests remedies with approximately 80% accuracy. GitHub’s CodeQL (originally developed by Semmle) is a widely used tool in this space; it allows teams to query their codebase for known vulnerability patterns and logical variants across the entire repository. Microsoft’s security response team has used CodeQL to find variants of critical vulnerabilities, enabling faster response and reducing the blast radius of discovered flaws.

4. Prioritizing and Triaging Security Vulnerabilities

Security scanners can generate thousands of findings per scan. Without intelligent triage, teams waste significant time on false positives or low-risk issues while genuinely critical vulnerabilities wait in the queue.

ML models trained on historical vulnerability data and developer remediation patterns can score and rank findings by their likelihood of being true positives and by their actual exploitability in context. Maty Siman, CTO of Checkmarx, has described this challenge and the ML-based solution clearly: models learn which vulnerability types have a statistically higher rate of being genuine threats, allowing AppSec and development teams to focus remediation effort where it matters most. Siman notes that the security landscape evolves continuously, and ML systems that adapt at the same pace are essential for teams that cannot manually review every alert.

5. Automated Test Case Generation and Optimization

Writing and maintaining test cases is a specialized skill that sits at the intersection of deep product knowledge and software engineering. As codebases grow, test suites become difficult to maintain: coverage gaps emerge, redundant tests accumulate, and skilled testers are perpetually backlogged.

AI-based testing tools address this by auto-generating test cases based on analysis of code structure, user flows, and historical defect patterns. These tools can also identify overlapping test coverage, remove redundant cases, and shift the focus from reactive defect detection to proactive defect prevention—predicting which code paths are most likely to fail under specific load or input conditions before tests are run manually.

6. Managing Complex Module Dependencies

Large enterprise applications consist of dozens or hundreds of interdependent modules. A change in one area can produce unexpected failures in others, and mapping these dependencies manually is error-prone and time-consuming.

AI-based development platforms can automatically map and monitor inter-module dependencies, flagging high-risk change propagation paths before a release. This improves overall product quality and gives release managers better information about which components need additional testing when a given module is modified.

7. Post-Release Defect Prediction and Root Cause Analysis

Even with rigorous pre-release QA, defects emerge in production. The cost of identifying, diagnosing, and fixing production defects is significantly higher than catching the same issue during development. AI is being applied to predict which areas of a released application are most likely to fail in specific customer environments before they actually do.

Beyond prediction, AI tools assist with root cause analysis—correlating logs, traces, and metrics to pinpoint the origin of a failure faster than manual investigation allows. One enterprise CRM team reported a 72% reduction in time-to-restore in customer environments after adopting AI-assisted root cause analysis. Other teams have used AI to auto-configure application settings to optimize performance for specific deployment profiles.

8. Anomaly Detection and Real-Time Security Monitoring

ML models can establish a statistical baseline of normal behavior across application performance metrics, code commit patterns, API call volumes, and infrastructure telemetry. Deviations from that baseline—anomalies—are flagged in real time for human review.

Checkmarx’s Maty Siman has described this approach: given a baseline of normal developer and application activity, ML can identify and surface anomalies that indicate either a security threat or an emerging performance issue before it affects end users. This kind of continuous, automated vigilance is not achievable at scale through manual monitoring alone.

9. Cross-System Analytics for Process Improvement

Advanced DevOps teams are applying AI not just to code, but to the development process itself. By aggregating data from version control systems, CI/CD pipelines, APM platforms, issue trackers, and release management tools, ML models can surface patterns that explain why some projects deliver on time with high quality while others fall into prolonged review and rework cycles.

Enterprise DevOps teams in supply chain management have used supervised ML to identify these patterns and build genuinely data-driven engineering cultures. The goal is to move from anecdotal postmortems to quantitative, predictive process improvement—understanding what leading indicators predict a troubled sprint before the sprint ends.

10. Improving Traceability and Cross-Team Collaboration

In distributed teams spanning multiple geographies, keeping requirements, code changes, test outcomes, and release decisions aligned is a persistent challenge. AI-driven traceability tools help maintain clear links between user requirements, implemented features, test coverage, and shipped releases—making it possible to identify exactly where a gap in collaboration or data integration caused a quality or schedule problem.

AI-driven insights also help distributed teams understand how shared specifications need to account for localization requirements, region-specific performance constraints, and unique customer configurations—surfacing these gaps earlier in the cycle rather than at release time.

Building a Framework for AI-Driven DevOps

Adopting individual AI tools is only part of the opportunity. The teams seeing the greatest gains are those that have integrated AI capabilities into a coherent DevOps strategy—one that embeds security into every development phase (DevSecOps), incorporates quality assurance throughout the pipeline rather than as a gate at the end, and uses analytics to drive continuous improvement.

Frameworks that help enterprises operationalize this integration—connecting AI capabilities across planning, development, testing, security, release, and operations—are an active area of vendor development. The core principle is consistent: AI delivers the most value in DevOps when it is woven into the workflow rather than bolted on as a separate tool.

Conclusion

AI is accelerating DevOps across every phase of the SDLC—not by replacing engineers, but by handling the repetitive, time-consuming tasks that take them away from high-value work. Code autosuggestion, intelligent test generation, ML-based vulnerability triage, anomaly detection, and cross-system process analytics are all delivering measurable improvements in speed, quality, and security today.

The teams realizing the greatest benefit are those treating AI adoption as a strategic, integrated initiative rather than a series of point-tool experiments. As the underlying models improve and more SDLC data becomes available to train them, the gap between AI-augmented DevOps teams and those operating without these capabilities will continue to widen.

What DevOps Automation Actually Means

Amazon Web Services describes the DevOps model as a combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity. Crucially, it also enables development teams to evolve and improve products faster than teams relying on traditional software development and infrastructure management processes.

In practice, DevOps automation spans several layers of the software delivery lifecycle:

• Development: Automated build pipelines, code linting, and static analysis catch issues before they reach review.
• Testing: Automated unit, integration, and end-to-end tests eliminate the manual testing bottleneck that slows releases.
• Infrastructure: Infrastructure-as-Code (IaC) tools allow teams to provision, configure, and tear down environments consistently and repeatably.
• Deployment: Continuous delivery pipelines push validated changes to production — or as close to it as possible — with minimal human intervention.
• Operations: Automated monitoring, alerting, and self-healing systems reduce the operational burden on engineering teams after deployment.

According to Pushpraj Kumar, a business analyst at iFour Technolab, DevOps continuous delivery and consistent cadence of software releases play a major role in the operations strategy of any IT organization. “DevOps accelerates digital services, continuous improvement, and operational flexibility, and could provide innovative and cost-effective ways for delivering high-value automation across development and operations,” he noted.

The Reality: Most Organisations Are Still Getting the Basics Right

There is a temptation to jump straight to discussions of AI-augmented pipelines and autonomous operations. But Michael “Fritz” Fritzius of Arch DevOps offers a useful counterpoint: many companies have not yet mastered conventional DevOps, which makes advanced capabilities like AI or ML even harder to realize.

CTOs, CIOs, and engineering leaders regularly struggle to make sound decisions under deadline pressure. “More often than not, those decisions are rooted in fear, which causes people to guess wrong about the problems needing to be solved, and it creates a mess,” Fritzius said. The consequence is that tooling decisions get made before process and culture are in place — a pattern that undermines the value of any automation investment.

His advice is pragmatic: focus on executing core DevOps well before chasing the next wave of innovation. Automation only amplifies whatever process it is built on top of. If that process is broken, automation makes it worse, faster.

DevOps Is a Culture, Not Just a Toolchain

Rick Stewart, chief software technologist at DLT Solutions, emphasises that DevOps is fundamentally a cultural movement — not a product category. It is built on shared understanding and shared responsibility between developers and operations teams for the software and services they collectively produce.

Organisations that have reached a mature DevOps culture tend to display recognisable characteristics:

• Transparency: Work in progress, failure rates, and deployment frequency are visible to all stakeholders, not just engineering teams.
• Continuous improvement: Teams actively look for inefficiencies in every iteration, not just during post-mortems.
• Automation by default: Manual steps are treated as technical debt to be eliminated, not as the safe or reliable option.
• Infrastructure elasticity: Automated scaling allows resources to expand and contract with demand, converting capital costs into operational costs.

Stewart points out that software delivery is a highly repetitive process, and the removal of manual steps is essential for deploying the most important features frequently and in small increments. Manual activities introduce human error and create bottlenecks — particularly in testing — that compound as teams grow.

“There is a reason why DevOps is expressed as an infinite loop,” Stewart said, referring to the familiar DevOps lifecycle diagram. “Those activities are executed repeatedly, providing feedback to teams and exposing any inefficiencies that can be addressed in a subsequent iteration.”

He also makes an often-overlooked point: a characteristic of a healthy DevOps culture is that it produces joy — not in a trivial sense, but because end users get the features they need on infrastructure that stays up. “By increasing the ability to deliver important features fast on resilient infrastructure, DevOps addresses all stakeholders’ primary needs, which leads to happiness in the workplace.”

Speed, Scale, and the Role of Microservices

Eva Tuczai, then advanced solutions product management and engagement manager at Turbonomic, frames DevOps automation as the intersection of two forces: scale (many independent, loosely-coupled services) and speed (automated pipelines that minimise human overhead at each stage).

The goal is to bring services to market quickly by breaking applications into small, discrete components that can be independently changed, tested, and deployed. How quickly any given service can move through the pipeline depends on how well isolated it is from other services — tight coupling between services is one of the most common constraints on DevOps velocity in practice.

Tuczai identified two emerging specialisations forming within mature DevOps organisations:

• TenantOps: Specialists who help application teams onboard to and best leverage internal platforms and pipelines. They provide operational support to app teams on maintaining performance within the platform and using the DevOps process efficiently.
• ResourceOps: Specialists focused on application performance and end-user experience, ensuring that IT resources meet application requirements while avoiding the excessive overprovisioning that is common when teams prioritise risk avoidance over efficiency.

What Comes Next: AI, Compliance, and Value Stream Management

Richard Hawes, director of product marketing for DevOps at ServiceNow, argues that automation, AI, and ML will enable DevOps to scale in large enterprises in ways that were previously impractical — accelerating digital transformation for both those organisations and the customers they serve.

However, he notes a meaningful friction point: even where individual teams become agile and can iterate rapidly, the pace of innovation slows when those teams must participate in the broader business processes that surround software delivery. This friction is most acute in large, regulated organisations, where risk aversion and compliance requirements add gates that were never designed with DevOps velocity in mind.

The areas he sees as most ripe for automation in the near term include:

• Production release gates: Automating the change approval activities traditionally associated with ITIL-style change management, so compliance checks become part of the pipeline rather than a manual step that halts it.
• Continuous compliance: Integrating corporate risk management requirements — security scanning, licence checks, audit evidence collection — directly into the CI/CD pipeline so that compliance is verified continuously rather than audited after the fact.
• Cross-tool management: As organisations standardise on DevOps platforms, Value Stream Management tooling is emerging to provide visibility and governance across the diverse toolchains that exist within even a single large organisation.

“DevOps brings the business closer to the technologists delivering the solutions by focusing work into teams that own a business product or are directly aligned with a value stream,” Hawes said. That alignment — between business outcomes and engineering work — is ultimately what DevOps automation is in service of.

Key Takeaways

• DevOps automation covers the full delivery lifecycle: build, test, infrastructure, deployment, and operations.
• Culture and process must come before tooling. Automation amplifies whatever process it sits on top of.
• Loose coupling between services is a prerequisite for high DevOps velocity — monolithic architectures constrain pipeline speed regardless of tooling.
• Emerging specialisations (TenantOps, ResourceOps) reflect the maturation of DevOps practice in larger organisations.
• The next frontier is automating compliance, release governance, and cross-team coordination — not just code deployment.
• AI and ML hold genuine promise for DevOps, but most organisations will need to consolidate their core automation practices first.

1. Post Graduate Certification in Big Data Analytics — E&ICT, IIT Guwahati

This programme is designed for graduates who want to build a career in Big Data Analytics, Data Science, or Machine Learning. It spans 11 subject areas including Statistics for Analytics, Data Analytics with Excel and SQL, Python, Java, Linux, Big Data and Hadoop, Apache Spark, MongoDB, Business Intelligence and Data Mining, and Tableau.

• Duration: 9 months
• Price: ₹2,25,000
• Eligibility: Graduates with a minimum of 50% marks in their undergraduate degree
• Certificate: Joint certificate from E&ICT IIT Guwahati and IBM upon completion

2. Machine Learning — Coursera

One of the most recognised introductory machine learning courses available online, this Coursera offering covers logistic regression, artificial neural networks, and a range of ML algorithms. The course balances theoretical foundations with practical implementation exercises, making it suitable for learners who want to apply ML techniques to real problems quickly.

• URL: coursera.org/learn/machine-learning
• Duration: Approximately 64 hours
• Price: ₹4,508 (pricing may vary; audit option may be free)
• Eligibility: Open to all learners
• Certificate: Electronic certificate, shareable on LinkedIn

3. Data Science Architect Master’s Course — Intellipaat

This master’s-level programme covers the full breadth of Data Science tooling: R, Hadoop, Apache Spark, Scala, Deep Learning, Tableau, SAS, SQL, and MongoDB. The curriculum includes 10 core courses, 53 industry projects, and one Capstone project. Learners also receive five IBM-co-created self-paced courses covering Deep Learning with TensorFlow, chatbot development with Watson Assistant, R for Data Science, Spark MLlib, and Python for Data Science.

• URL: intellipaat.com — Data Science Architect
• Duration: 232 hours of online instructor-led training
• Price: ₹62,000
• Eligibility: No prerequisites
• Certificate: IBM certificate upon completion

4. Deep Learning Explained — edX

This edX course provides a practical introduction to deep learning. Topics include multi-class classification using logistic regression, handwritten digit recognition with deep neural networks and convolutional networks, time-series forecasting with recurrent networks, and text processing using LSTM units. It is a good entry point for those with a data science background who want to move into neural network architectures.

• URL: edx.org — Deep Learning Explained
• Duration: 6 weeks
• Price: Free to audit; verified certificate costs $99
• Eligibility: Basic programming skills and working knowledge of data science recommended

5. Cloud and DevOps Architect Master’s Course — Intellipaat

This programme is directly relevant to DevOps engineers and cloud practitioners. It covers AWS, Azure, DevOps toolchains, Python, Java, Splunk, and Linux across 11 courses, 26 industry projects, and a Capstone project. Learners also receive official Microsoft course material on integrating on-premises identity infrastructure with Azure and implementing security in Azure development solutions.

• URL: intellipaat.com — Cloud & DevOps Architect
• Duration: 147 hours of online instructor-led training
• Price: ₹56,900
• Eligibility: No prerequisites
• Certificate: Joint IBM and Microsoft certificate upon completion

6. Mastering Python — Networking and Security — Udemy

Python is the de-facto scripting language for network automation and security tooling. This Udemy course focuses specifically on using Python for network scripting and security tasks. It covers script creation, threading, exception handling, and leveraging Python networking libraries to build practical automation scripts.

• URL: udemy.com — Mastering Python: Networking and Security
• Duration: Approximately 5 hours of on-demand video
• Price: ₹360 (Udemy prices fluctuate; check current listing)
• Eligibility: Basic programming awareness helpful but not strictly required

7. Artificial Intelligence Master’s Course — Intellipaat

This AI-focused master’s programme covers Data Science, Python, R, Statistics, Natural Language Processing, SAS, Excel-based analytics, and Tableau. It is designed for learners who want a broad AI and ML skill set without a strict programming prerequisite, though basic programming knowledge will help you progress faster.

• URL: intellipaat.com — Artificial Intelligence Master’s Course
• Duration: 166 hours of online instructor-led training
• Price: ₹46,000
• Eligibility: No prerequisites; basic programming knowledge is an advantage
• Certificate: IBM certificate upon completion

8. Web Development with Django and AngularJS — Pluralsight

This Pluralsight course teaches full-stack web development by combining Django on the back end with AngularJS on the front end. Students build a complete web application, learn to connect front-end JavaScript to back-end Python using REST and AJAX, and finish with a working, interactive application they can extend independently.

• URL: pluralsight.com — Django and AngularJS Web Development
• Duration: 3 hours 42 minutes
• Price: Included in Pluralsight subscription (approximately $19/month at time of writing; check current pricing)
• Eligibility: Open to all learners

9. Business Intelligence Architect Master’s Course — Intellipaat

For data professionals who work closer to the business layer, this BI-focused programme covers Informatica, Tableau, MSBI, Power BI, MS SQL, data warehousing and Erwin modelling, Azure Data Factory, and SQL DBA administration. The programme includes 9 core courses and 32 industry-based projects.

• URL: intellipaat.com — Business Intelligence Master’s Course
• Duration: 150 hours of online instructor-led training
• Price: ₹56,000
• Eligibility: Open to all learners
• Certificate: Microsoft certificate upon completion

10. Intro to Machine Learning with TensorFlow — Udacity

This introductory programme covers foundational machine learning algorithms, starting from data cleaning and supervised learning models and progressing to deep learning and unsupervised techniques. Practical exercises are integrated at each stage, giving learners hands-on experience with TensorFlow alongside conceptual understanding.

• Duration: Approximately 3 months
• Eligibility: Basic Python and statistics knowledge recommended

Choosing the Right Course for Your Role

The courses above span a wide range of investment levels — from free audits to multi-month master’s programmes. Before enrolling, consider the following:

• DevOps / Cloud engineers will get the most immediate value from the Cloud and DevOps Architect Master’s Course or the Python Networking and Security course.
• Data engineers and analysts should look at the Big Data Analytics certification or the Data Science Architect programme.
• Machine learning practitioners can start with the Coursera ML course or the TensorFlow intro and graduate to the Deep Learning Explained or AI Master’s course.
• Full-stack developers moving into data-driven products will benefit from the Django and AngularJS course alongside a BI or SQL foundation.

Prices and course structures change regularly. Always verify the current syllabus, pricing, and certification status directly on the provider’s website before enrolling.

The Growing Intersection of AI and High-Performance Computing

Artificial intelligence is no longer a research curiosity—it is deeply embedded in everyday business operations. From natural-language chatbots in customer service centers to autonomous vehicles in logistics and distribution, AI-driven capabilities are reshaping how organizations function at every level.

This rapid adoption has created an equally rapid surge in demand for high-performance computing (HPC). Training deep learning models, running real-time AI inferencing, and performing analytics on massive datasets all require enormous computational resources—resources that traditional enterprise infrastructure was never designed to provide at scale.

HPC-Enabled AI: A Fast-Growing Market Segment

According to Hyperion Research, workloads including machine learning, deep learning, inferencing, training, graph analytics, and high-performance data analytics (HPDA) have all experienced robust growth in both budget allocations and organizational focus. Hyperion projected that HPC-enabled AI would grow at more than 30 percent annually to reach a $3.5 billion market by 2024.

Hyperion also noted a structural shift in where these workloads actually run. The percentage of HPC workloads executing in the cloud reached approximately 20% in 2019, a significant jump that reflects changing infrastructure priorities across the industry.

Why HPC Workloads Are Moving to the Cloud

Several converging factors explain why cloud environments are becoming a viable—and sometimes preferred—platform for HPC and AI workloads:

• Hybrid and multi-cloud maturity: Cloud service providers (CSPs) now offer hybrid and multi-cloud options specifically designed to give HPC users a near-seamless computing environment that spans on-premises hardware and cloud resources. This reduces the friction historically associated with migrating HPC jobs off dedicated clusters.
• Elastic capacity for complex workloads: As HPC applications grow in complexity—particularly when new AI techniques are layered onto traditional modeling and simulation workflows—the ability to burst into the cloud provides a more flexible and responsive computing environment without requiring permanent capital investment in hardware.
• Unified management across providers: Multi-cloud capabilities allow HPC users to manage resources from multiple CSPs through a single management plane. This is especially attractive to global enterprises that rely on different cloud providers in different geographic regions for latency, compliance, or cost reasons.
• Data gravity: For some AI workflows, the training data already resides in a cloud environment—collected from IoT sensors or assembled from multiple cloud-hosted datasets. Moving that data to an on-premises cluster can be expensive and slow. Running the workload where the data lives is often more practical.

The Role of HPC Expertise in Cloud Adoption

One barrier to cloud adoption for HPC teams has historically been the knowledge gap: HPC practitioners are experts in parallel computing and job scheduling, but may be less familiar with cloud instance selection, network topology, and cost optimization. Recognizing this, many CSPs now offer dedicated HPC support teams, in-house experts, and professional services to help users select the right instance types and configurations for specific workload profiles.

Hardware and software vendors have also built dedicated facilities to close this gap. Hyperion Research noted Dell’s HPC & AI Innovation Lab as one example of an environment that combines purpose-built hardware and software infrastructure with a staff of engineers, computer scientists, and domain experts engaged in the HPC community. The goal is to help end users understand best practices for solving HPC and AI problems, whether they are running AI alongside traditional simulation workloads or exploring entirely new application domains.

What This Means for DevOps and Platform Engineering Teams

For DevOps and platform engineering teams, the convergence of AI, HPC, and cloud infrastructure introduces new operational requirements:

• GPU and accelerator orchestration: AI training jobs typically require GPU instances. Teams need pipelines that can provision, schedule, and tear down GPU nodes efficiently—tools like Kubernetes with GPU operator support or cloud-native batch services become important.
• Cost management at scale: HPC workloads can consume enormous amounts of compute in short bursts. Spot or preemptible instances, combined with checkpointing strategies, are common approaches to managing cost without sacrificing throughput.
• Data pipeline reliability: When data gravity drives workload placement, robust, observable data pipelines between storage systems and compute clusters become a critical infrastructure concern.
• Hybrid connectivity: Organizations that retain on-premises HPC clusters while bursting to the cloud need low-latency, high-bandwidth connectivity—typically through dedicated interconnects or SD-WAN solutions—to avoid bottlenecks.

Key Takeaways

The movement of HPC workloads into cloud environments is being driven by the computational demands of AI, the maturity of hybrid cloud offerings, and the practical reality of data gravity. As CSPs invest more heavily in HPC-specific support and tooling, the barrier to running demanding AI and analytics workloads in the cloud continues to drop. For engineering teams, this shift means adapting infrastructure practices to support elastic, accelerator-driven, data-intensive workloads—while maintaining the cost controls and observability that production environments require.

Overview

Udacity and Amazon Web Services (AWS) announced a partnership in May 2020 to offer a two-phase scholarship program designed to help prepare the global workforce for growing demand in machine learning (ML) roles. The initiative was structured to lower the barrier to entry for ML education, offering free foundational training to all applicants and a full Nanodegree scholarship to the highest performers.

How the Two-Phase Scholarship Worked

The scholarship was delivered in two sequential phases:

Phase 1 — AWS Machine Learning Foundations Course

Every applicant received a scholarship to the AWS Machine Learning Foundations Course. This foundational course covered:

• Core software engineering principles relevant to ML workflows
• AWS machine learning concepts and services
• Production-level coding practices
• Object-oriented programming techniques

All students who completed the foundation course received a special certificate of completion. Enrollment for this phase was scheduled to open on May 19, 2020, with an option to sign up in advance for launch notifications.

Phase 2 — Machine Learning Engineer Nanodegree

The top 325 performers from the foundation course — determined by successful course completion and high scores on a subsequent assessment — were awarded a full scholarship to Udacity’s Machine Learning Engineer Nanodegree program. This advanced program offered:

• Hands-on experience with AWS machine learning tools and services
• Project-based learning with result-oriented assignments
• A deeper engineering focus on deploying and scaling ML models

The Nanodegree scholarship was scheduled to begin on August 19, 2020.

Why This Program Mattered

The partnership reflected a broader industry trend of cloud providers and online learning platforms collaborating to address skills gaps in data science and machine learning. By covering the foundational course for all applicants — not just a select few — the program removed the initial financial obstacle that often discourages early-career professionals from exploring ML. The competitive path to the Nanodegree also gave motivated learners a clear, merit-based incentive to perform well in the foundational stage.

Who Should Have Applied

The program was aimed at a broad audience, including:

• Software engineers looking to transition into ML engineering roles
• DevOps practitioners wanting to understand ML model deployment on AWS
• Developers with basic programming experience seeking structured ML training
• Students and professionals exploring cloud-based AI/ML services

Related Video

Note on Current Availability

This scholarship program was announced in May 2020. Enrollment windows and program availability described above reflect the original announcement dates and may no longer be active. Readers interested in current Udacity–AWS scholarship offerings should check the Udacity website and AWS Training and Certification pages for up-to-date programs.

Background: A Joint PyTorch Push from AWS and Facebook

AWS and Facebook announced two new open-source projects built around PyTorch, the widely adopted machine learning framework originally developed by Facebook AI Research. The announcement signalled a formal collaboration between two organisations that each operate PyTorch workloads at a very large scale, and it produced two distinct tools aimed at different parts of the ML engineering lifecycle: TorchServe for model serving and TorchElastic for distributed training resilience.

TorchServe: A Model Server Built Specifically for PyTorch

TorchServe is a model-serving framework designed to make it straightforward to deploy PyTorch models to production. Before its release, practitioners who wanted to serve PyTorch models had to rely on generic solutions such as TensorFlow Serving or the AWS Multi Model Server — both of which were not tailored to the idioms and workflows that PyTorch developers expect.

According to Bratin Saha, AWS VP and GM for Machine Learning Services, the primary motivation was community demand. Adapting an existing model server would have meant compromising on PyTorch-specific optimisations and on the API design that PyTorch developers are accustomed to. TorchServe addresses this by exposing an API surface aligned with how PyTorch models are structured and exported.

Saha also confirmed that the open-source release started from the internal model server that AWS uses within its own infrastructure — specifically work that originated inside Amazon SageMaker — and was then iterated on with Facebook’s engineering team and the wider community before being published.

Key Capabilities of TorchServe

• Multi-model serving: Serve multiple PyTorch models simultaneously from a single server process.
• REST and gRPC APIs: Standard inference endpoints that align with production service expectations.
• Model versioning and management: Register, update and retire model versions without restarting the server.
• Logging and metrics: Built-in hooks for operational observability.
• Custom handlers: Pre- and post-processing logic can be attached per model, keeping business logic close to the model artefact.

TorchElastic: Fault-Tolerant Distributed Training on Kubernetes

TorchElastic addresses a different problem: making distributed PyTorch training jobs resilient to node failures and to changes in cluster size at runtime. This is particularly relevant when training on cost-optimised infrastructure such as AWS EC2 Spot Instances, which can be interrupted with minimal notice, or when using AWS Elastic Kubernetes Service (EKS) clusters that autoscale.

Traditional distributed ML training frameworks typically assume a fixed worker count for the entire duration of a job. If one worker fails, the entire job fails. TorchElastic breaks this assumption by allowing the training process to continue — or gracefully restart from a checkpoint — when the number of available workers changes.

Why This Matters for Cost and Reliability

• Spot Instance compatibility: Spot or preemptible instances are significantly cheaper than on-demand instances, but they can be reclaimed by the cloud provider. TorchElastic enables training jobs to tolerate this without total loss of progress.
• Dynamic scaling: Worker counts can grow or shrink during training, making it easier to respond to changing cluster availability.
• Kubernetes-native: TorchElastic integrates with Kubernetes, which is the dominant container orchestration layer in enterprise ML infrastructure.
• Developer control: Unlike the fully managed equivalent inside SageMaker, TorchElastic gives teams direct access to the underlying mechanics, which is useful when running on-premises or on third-party clouds.

Why AWS and Facebook Collaborated

Bill Jia, Facebook’s VP of AI Infrastructure, explained the rationale simply: a large proportion of researchers and enterprise teams who use PyTorch also run their workloads on AWS. By investing jointly in shared infrastructure tooling, both companies benefit — and more importantly, the broader PyTorch community gains production-grade tooling that neither organisation would have had as strong an incentive to open-source alone.

For AWS, this announcement also represents a continuation of its engagement with the open-source ML ecosystem. The company has been a significant contributor to Apache MXNet and has contributed to projects including Jupyter, TensorFlow, and libraries such as NumPy. Adding TorchServe to that list reflects the practical reality that PyTorch has become the framework of choice for a large share of ML research and is increasingly adopted in production settings.

Where to Find the Projects

Both TorchServe and TorchElastic are open source and hosted under the PyTorch organisation. You can find TorchServe at the pytorch/serve GitHub repository and TorchElastic at pytorch/elastic. Official documentation is available through the PyTorch website.

Summary

Making Sense of Azure IoT

Azure’s IoT portfolio has grown significantly over the years, giving architects and administrators a broad set of services to build end-to-end IoT solutions. As explored by Chris Pietschmann on Build5Nines, understanding how these services relate to one another is the first step to designing a solid IoT architecture.

Key Azure IoT Services

• Azure IoT Central – A managed application platform for building and deploying IoT solutions without deep cloud expertise.
• Azure IoT Hub – A scalable, bidirectional messaging broker that connects IoT devices to backend services.
• IoT Device Provisioning Service (DPS) – Enables zero-touch, just-in-time provisioning of devices to IoT Hub.
• Azure Maps – Geospatial services for location-aware IoT applications.
• Azure Time Series Insights – A managed service for storing, visualizing, and querying time-series data from IoT devices.
• Azure IoT Edge – Moves cloud intelligence (AI, analytics, business logic) closer to the device, reducing latency and bandwidth usage.
• Azure Sphere – A secured, high-level microcontroller platform for connected devices.
• Azure Digital Twins – Creates graph-based models of physical environments, enabling richer analytics and simulation.

The Lambda Architecture Pattern for IoT

A recurring design pattern in large-scale IoT systems is the Lambda Architecture. This pattern addresses the challenge of processing high-velocity data streams from thousands or millions of devices while still supporting historical batch analysis.

Lambda Architecture has three layers:

• Batch layer – Processes and reprocesses the full historical dataset at scheduled intervals, producing accurate but latency-tolerant results.
• Speed (stream) layer – Processes incoming data in near real-time, compensating for the latency of the batch layer.
• Serving layer – Merges batch and real-time views, making results available for queries.

On Azure, this pattern is commonly implemented using IoT Hub for ingestion, Azure Stream Analytics or Azure Databricks for real-time processing, Azure Data Lake Storage for the batch layer, and Azure Synapse Analytics or Cosmos DB for the serving layer.

Planning Web App Security on Azure

Karim Vaes posed a common architectural question: “What do I put in front of my web application to secure it?” Azure offers several overlapping services, and choosing correctly requires understanding their distinct roles.

Application Gateway vs. Azure Front Door

Both Application Gateway and Azure Front Door can be configured as a Web Application Firewall (WAF), but they differ in important ways:

Front Door vs. Traffic Manager

Both services support multi-region deployments, but through different mechanisms:

• Azure Traffic Manager is DNS-based. It redirects clients to the appropriate regional endpoint at DNS resolution time. It does not proxy traffic, so it cannot cache or inspect HTTP payloads.
• Azure Front Door is a reverse proxy operating at Layer 7 (HTTP/HTTPS). Because it terminates connections, it can cache content, perform SSL offloading, and inspect or rewrite requests.

Caching: Front Door vs. Azure CDN

Both Azure Front Door and Azure CDN provide edge caching, but their primary use cases differ:

• Azure Front Door caches content to accelerate web application delivery. It is optimized for dynamic and semi-static web workloads.
• Azure CDN is purpose-built for distributing large static files globally from edge points of presence (PoPs), making it the better fit for high-throughput media or file distribution scenarios.

All of the services discussed — Application Gateway, Front Door, Traffic Manager, and Azure CDN — include some form of DDoS attack mitigation, either natively or through integration with Azure DDoS Protection.

Creating an Azure Storage Account with PowerShell

Chris Pietschmann on Build5Nines walked through using the Azure PowerShell Az module to create Storage Accounts and Storage Containers programmatically. Storage Accounts underpin a wide range of Azure services — virtual machine disks, diagnostic logs, Blob storage, and more.

Storage Account Naming Rules

Storage Account names must follow stricter conventions than most Azure resources:

• Between 3 and 24 characters in length.
• Lowercase letters and numbers only — no hyphens, underscores, or special characters.
• Must be globally unique across all Azure customers.

Creating a Storage Account

# Install the Az.Storage module if not already present
Install-Module -Name Az.Storage -Scope CurrentUser

# Log in to Azure
Connect-AzAccount

# Create a new Storage Account
New-AzStorageAccount `
  -ResourceGroupName "myResourceGroup" `
  -Name "mystorageaccount" `
  -Location "eastus" `
  -SkuName "Standard_LRS" `
  -Kind "StorageV2"

Creating a Storage Container

Before creating a container, you must retrieve a Storage Account context. This context tells subsequent cmdlets which account to target.

# Retrieve the Storage Account context
$storageAccount = Get-AzStorageAccount `
  -ResourceGroupName "myResourceGroup" `
  -Name "mystorageaccount"

$ctx = $storageAccount.Context

# Create a new Blob container
New-AzStorageContainer `
  -Name "mycontainer" `
  -Context $ctx `
  -Permission Off

The -Permission parameter controls public access: Off means no public access, Blob allows public read access to blobs only, and Container allows public read access to the container and its blobs.

Pietschmann also covered Storage Account pricing tiers (Hot, Cool, and Archive access tiers for Blob storage) and the importance of the Az.Storage PowerShell module, which is part of the broader Az module suite.

Preparing for the Azure Architect Design Exam

Microsoft Senior Cloud Advocate Thomas Maurer shared preparation advice for the Azure Architect Technologies and Azure Architect Design certification exams.

Recommended Study Resources

• Microsoft Learn – Free, structured learning paths aligned to exam objectives. A recommended starting point for anyone new to Azure certifications.
• Microsoft Docs – The official technical documentation. Exam questions are frequently grounded in documented service behaviors and best practices, making Docs essential reading.

Key Exam Topic Areas

Both the Architect Technologies and Architect Design exams cover a broad set of domains. Based on the advice shared by Maurer, major topic areas include:

• Monitoring and observability – Logging strategies, Azure Monitor, Application Insights, and cost optimization.
• Identity and security – Azure Active Directory, role-based access control (RBAC), managed identities, and Key Vault.
• Data storage – Choosing between SQL, NoSQL, Blob, and other storage services based on workload requirements.
• Business continuity and disaster recovery – Backup strategies, geo-redundancy, availability zones, and SLA targets.
• Infrastructure and networking – Virtual networks, hybrid connectivity, load balancing, and compute options.

Candidates are advised to work through scenario-based questions and understand not just what each service does, but when to choose it over alternatives — a key skill tested in the Architect Design exam in particular.