Both DevOps and MLOps are essential practices that drive automation, efficiency, and collaboration across the software development lifecycle. While they share some similarities, such as the emphasis on automation and continuous delivery, they serve different purposes. DevOps is primarily focused on improving the collaboration between development and operations teams to streamline software deployment, while MLOps focuses on the deployment, monitoring, and maintenance of machine learning models in production.

This post will explore the key differences between DevOps and MLOps, their major features, and how they address unique challenges within their respective domains.

1. Definition and Purpose

DevOps:

• Collaboration and Automation:
  • DevOps is a set of practices and cultural philosophies that bring together software development (Dev) and IT operations (Ops) teams. It emphasizes collaboration, continuous integration (CI), continuous delivery (CD), and automation to speed up the development and deployment of applications.
• Goal: Accelerate Software Delivery:
  • DevOps aims to accelerate the delivery of high-quality software by automating the deployment pipeline, enabling faster release cycles, and improving collaboration across teams.
• Tools and Techniques:
  • DevOps uses a range of tools like Jenkins, Docker, Kubernetes, Ansible, and GitLab for automating various stages of the development and deployment lifecycle.

MLOps:

• Machine Learning Workflow Management:
  • MLOps is a set of practices for the end-to-end lifecycle management of machine learning models, from development and training to deployment and monitoring in production. It focuses on automating and streamlining the workflow for machine learning projects.
• Goal: Manage ML Models at Scale:
  • MLOps aims to improve the collaboration between data scientists, machine learning engineers, and IT operations teams to ensure that machine learning models can be efficiently developed, deployed, and maintained in production environments.
• Tools and Techniques:
  • MLOps leverages tools like TensorFlow, KubeFlow, MLflow, Kubeflow Pipelines, and Seldon to automate model training, versioning, deployment, and monitoring.

2. Focus Areas and Objectives

DevOps:

• Continuous Integration and Continuous Delivery (CI/CD):
  • DevOps focuses on automating the CI/CD pipeline to ensure that code is continuously tested, integrated, and deployed. This leads to faster and more reliable software releases.
• Infrastructure Automation:
  • DevOps places significant emphasis on automating infrastructure provisioning, scaling, and management. Tools like Terraform and AWS CloudFormation are commonly used to define infrastructure as code (IaC).
• Collaboration and Communication:
  • DevOps encourages communication between developers, IT operations, and other stakeholders to foster collaboration and reduce bottlenecks in the software lifecycle.

MLOps:

• Model Training and Versioning:
  • MLOps focuses on automating the training process, versioning machine learning models, and tracking experiments. This ensures that models can be consistently trained and improved over time.
• Model Deployment and Monitoring:
  • MLOps emphasizes automating the deployment of machine learning models into production, ensuring they run efficiently at scale. It also involves continuous monitoring to track model performance, detect data drift, and update models as needed.
• Collaboration between Data Scientists and Operations Teams:
  • MLOps seeks to bridge the gap between data science and operations teams, ensuring that machine learning workflows are streamlined and models are deployed with the same rigor and automation as traditional applications.

3. Automation and Pipelines

DevOps:

• Automated Software Delivery:
  • DevOps automates the entire software delivery pipeline, including building, testing, and deployment. Continuous integration (CI) and continuous delivery (CD) are at the core of DevOps automation, which allows for rapid, reliable software releases.
• Infrastructure Automation:
  • Automation of infrastructure provisioning and configuration management is a critical component of DevOps. Tools like Ansible, Chef, and Puppet enable infrastructure to be managed in a repeatable, automated manner.
• Automated Testing:
  • DevOps emphasizes automated testing to ensure that software changes are rigorously tested at each stage of the pipeline, reducing manual errors and ensuring quality.

MLOps:

• Automated Model Training and Testing:
  • MLOps automates the process of training, testing, and evaluating machine learning models. This includes hyperparameter tuning, cross-validation, and tracking model performance on different datasets.
• Model Deployment Pipelines:
  • MLOps includes automated pipelines for deploying machine learning models to production. These pipelines ensure that models are continuously integrated and delivered to production environments in an automated and repeatable manner.
• Model Monitoring and Retraining:
  • MLOps also automates the monitoring of model performance in production. When performance drops or data drift is detected, the pipeline triggers the retraining or updating of models to maintain accuracy.

4. Collaboration and Roles

DevOps:

• Development and Operations Collaboration:
  • In DevOps, developers and operations teams work together throughout the software lifecycle. This collaboration ensures that software is not only developed efficiently but also deployed, monitored, and maintained seamlessly.
• Shared Responsibility:
  • Both development and operations teams share the responsibility for the quality of the software, including infrastructure management, monitoring, and troubleshooting in production environments.
• Cross-Functional Teams:
  • DevOps fosters cross-functional teams that include developers, IT operations, and security professionals working together to deliver software faster and with fewer risks.

MLOps:

• Data Science and Operations Collaboration:
  • MLOps aims to improve the collaboration between data scientists, machine learning engineers, and IT operations teams. This collaboration ensures that machine learning models are seamlessly integrated into production environments and are continuously optimized.
• Roles in MLOps:
  • Key roles in MLOps include data scientists, who build and train machine learning models, ML engineers, who implement models into production, and IT operations teams, who manage the infrastructure and monitor model performance.
• Shared Responsibility:
  • Similar to DevOps, MLOps promotes shared responsibility between data scientists and operations teams to ensure that models are continuously deployed and maintained effectively.

5. Challenges and Complexity

DevOps:

• Tool Integration:
  • One of the challenges in DevOps is integrating a wide range of tools for various tasks such as CI/CD, monitoring, and configuration management. Ensuring that all tools work together seamlessly is critical to the success of DevOps practices.
• Scaling Infrastructure:
  • As applications grow, scaling the infrastructure to support increased demand can be challenging. DevOps teams need to ensure that the infrastructure can scale efficiently without causing disruptions.
• Cultural Resistance:
  • Organizations often face resistance to adopting DevOps due to its cultural shift and changes in roles and responsibilities. Overcoming these barriers requires leadership commitment, training, and clear communication.

MLOps:

• Data Quality and Availability:
  • MLOps faces unique challenges in ensuring that high-quality, labeled data is available for training and evaluating machine learning models. Poor data quality can lead to inaccurate models and performance issues.
• Model Drift and Monitoring:
  • Models deployed in production may become less accurate over time due to changes in data distribution (data drift). Continuous monitoring and retraining of models are necessary to maintain their performance, adding complexity to MLOps.
• Scalability of Models:
  • MLOps requires the ability to scale machine learning models efficiently. This includes scaling model training, deployment, and inference pipelines to handle large amounts of data and requests.

6. Integration and Use Cases

DevOps:

• Software Development and Deployment:
  • DevOps is primarily used in traditional software development environments to streamline application development, deployment, and maintenance. It is applicable to web applications, mobile apps, and enterprise systems.
• Infrastructure Management:
  • DevOps is also used to automate and manage cloud infrastructure, server provisioning, and configuration management, ensuring that infrastructure is both scalable and reliable.
• Continuous Monitoring:
  • DevOps practices include continuous monitoring of applications and infrastructure to detect and resolve issues quickly, ensuring high availability and minimal downtime.

MLOps:

• Machine Learning Model Deployment:
  • MLOps is specifically designed for deploying and maintaining machine learning models in production environments. It is widely used in applications such as recommendation systems, predictive analytics, and autonomous systems.
• Data Pipelines:
  • MLOps integrates data pipelines for continuous data collection, preprocessing, and feature engineering, ensuring that models are trained on up-to-date and relevant data.
• Model Optimization and Scaling:
  • MLOps practices ensure that models are optimized for performance and can scale to handle large datasets, enabling use cases like real-time predictions and dynamic model updates.

DevOps vs MLOps

In summary, DevOps and MLOps serve different but complementary purposes in modern software development and operations. DevOps focuses on automating software development and deployment processes, fostering collaboration between development and operations teams. MLOps, on the other hand, is specifically designed to manage the lifecycle of machine learning models, from training and deployment to monitoring and optimization.

While DevOps practices can support the software development lifecycle of traditional applications, MLOps brings specialized tools and workflows to handle the complexities of machine learning projects, ensuring that models are continuously deployed and maintained in production environments. By leveraging both DevOps and MLOps, organizations can optimize both their software and machine learning workflows for greater efficiency, scalability, and performance.

Definitions:

DevOps – DevOps is the change in culture and practices of the SDLC process which enable the organization to produce high-quality software and continuously deliver the software. It has been originated from two words, Dev and Ops. DevOps has some phases through which it works. DevOps is the collaboration between development and operation teams to ensure continuous integration, development, test, deployment, and monitor. After DevOps came the development and operation team is no longer working as ‘siloed’. DevOps is much better and faster than any other traditional model in the current era because it has removed the vulnerabilities between culture ad practices which was stopping other organizations to keep up with the time which was demanding a faster and reliable process.

DevSecOps – DevSecOps is for development, security, and operations. Its main motto is to make every employee working under a project consider security policies also important like other works, so the product will be saved. DevSecOps is all about security measures from the development phase to the monitor phase. Organizations can implement DevSecOps to break down silos between development, security, and operations so they can maintain the proper security over their products. DevSecOps uses some security testing tools to integrate into their CI/CD process like –
⦁ Static application security testing (SAST)
⦁ Software composition analysis (SCA)
⦁ Interactive application security testing (IAST)
⦁ Dynamic application security testing (DAST)

SRE – SRE has been originated from google company. SRE’s main aim is to solve the operational problems right from testing to monitoring software when it is deployed in the market. The main focus of creating it was to remove the vulnerabilities of operation parts to make the product effective and reliable. It removed the ‘silos’ from all departments and made the operation teams work together to produce an efficient product, e.g – Somewhere you might have experienced you have never felt any downtime (the app is not working properly) on your Google apps even when Google teams are pushing updates over that particular app. This is like the SRE team works making your apps more trustworthy without making you feel any disturbance. SRE works with the collaboration of software engineers. SRE somewhere automates Its operation tasks and accelerate software delivery with minimizing risks.

Best Resources to Learn DevOps, DevSecOps, and SRE.

There are some resources where you can learn about all three of them is Youtube, Blogs, platforms that provide certifications, Fb Group, etc. I am going to share some of the channels and place them.

Youtube – These are some Youtube channels Devopsschool, Debug.school, and artificial intelligence universe, where you can learn DevOps, DevSecOps, SRE. Here you will get videos frequently. You can expand your knowledge here because the videos which are uploaded here are directly from our classes where some students like you would be learning at that time. There are so many video’s has been uploaded till now related to this course. These channels have been made only for DevOps and their tools-related courses, So these channels are really helpful to learn DevOps, DevSecOps, and SRE-related topics and tools. We have premium membership as well that you can buy every month where you can get your questions answered on daily basis as well as you can get access to a full playlist of DevOps-related tools like Docker, Kubernetes, Python Etc, which you can get normally in youtube. This facility is only for a premium membership program.

Fb groups – This is the FB Group DevOpsSchool where you can get the details related to DevOps, DevSecOps, and SRE. Here we upload the contents related to courses and some knowledgeable things and other important information which can expand your knowledge as well as keep you aware related to DevOps, DevSecOps, and SRE. It’s a very important place which will not make you feel regret. DevOps India is having fun on this platform with more than 5000 members in very little time. This is a public group from all over the world who are interested in DevOps. It proactive and updated group which posts, share, comments daily. It has a strict rule not to promote any advertisement, it’s completely focused on updates, sharing information, and discussion about the topics. You can see almost all participants participate in comments to share their views, even though you can find a job as well where recruiters post their vacancies here.

Blogs – https://www.thedataops.org/what-is-devops-life-cycle/, https://www.scmgalaxy.com/tutorials/what-are-devops-devsecops-and-sre-and-differences-among-them/, https://www.aiuniverse.xyz/software-development-lifecycle-sdlc-beginners-guide/ these are three blogs link where you can get some DevOps, DevSecOps, and SRE knowledge as well as differences between them. So do check these blogs, it will help you more to understand all three of them. And also you can visit our BestDevops blog website where you will several blogs related to DevOps. Blogs have always been an important part of our learning that expands our knowledge in desired topics. So always be connected to these blogs if you are seeking knowledge. A blog is always written after seeing so much information in several places, so Blogs are really important to read because you might get that information that you can’t get anywhere else. It has always been seen google plays a much important role than any other platform, that’s why Google always tries to give you the actual and correct information to help you out. So do check the Blogs.

Websites – These are the websites of an institute DevOpsSchool, ScmGalaxy where you can get the details of the complete course related to DevOps, DevSecOps, and SRE. Also, you will get a tutorial tab that has been made to help participants with DevOps-related issues which can be accessed without any charges. DevOpsSchool provides training and certification for all three of them. This Platform is one of the best platforms in the world which provides training all over the world and has made some carriers as far now in DevOps. Some of the places where it provides the training are Hyderabad, Bangalore, London, Amsterdam, Mumbai, Singapore, San Francisco, Europe, Australia, etc. DevOpsSchool provides online and also offline classes to their students just to help them according to their comfortability. There are so many tabs on this website through you can do so many things like Solutions, classes, etc. You can contact us through this website as well that is of course to help you guys with your issues. Through this website you can request to become a trainer, a trainee or can get training for your employees as a company or consulting firm.

Certification – There are some certifications to enhance your skills and knowledge about DevOps, DevSecOps, and SRE. The certifications are – Master in DevOps Engineering (MDE) – Including DevSecOps and SRE , DevSecOps Certified Professional Online Training, Site Reliability Engineering (SRE) Certified Professional Training. There are several topics will be covered in these certifications as well as you will be provided real-life-based projects which will boost your carrier also it will be more helpful for your knowledge and resume. DevOpsSchool offers a more friendly and comfortable environment where you can free to ask anything related to topics and our trainers are ready to help you all the time with PDFs, video calls, and many more. For those persons who want to build their carrier in DevOps, these certifications are very important as they not only provide the knowledge they improve your skills with real-life-based projects as well. So get it if you want to become a professional on DevOps.

Best DevOps – This is a website where updates come daily related to DevOps and other technologies like what’s happening with that specific technology or DevOps right now, what updates have come, how to use that Etc. This is the place from where you can be the first to get the latest news. BestDevOps is a platform of DevOps updates and news where you can read about DevOps-related updates, events, news, tutorials, tips, and much more. Through the contents which have been uploaded here, you will be helped a lot if you are a DevOps enthusiast. Just be with this platform and you will be up to date on regular basis. We have gathered all the blogs from different companies, different country and experts of DevOps in one place just to help you guys. BestDevOps is the DevOps portal for a website that covers a broad range of areas in DevOps.

I hope this list will help you out to connect and hear from DevOps professionals who provided information about everything DevOps-related you needed to know.

Conclusion

In this blog, we discussed the Definitions of DevOps, DevSecOps, and SRE and the resources through which you can learn about all three of the courses. The above-given resources are very important to you guys because all are important channels to learn about DevOps, DevSecOps, and SRE. You can learn DevOps from all the resources free of cost but only DevOpsSchool is the channel where you can go for the certification in any specific tools or courses.

Training Place

DevOpsSchool is a very important platform to get trained as it is one of the best platforms and to get trained from the best ones is how feels only a trained person can say. So do check it once if you are looking for certification and to boost your carrier. you can learn more from the above-described details.

Many organizations are struggling to realize well-engineered DevOps, even ‘the first way’ of DevOps – continuous flow.

DevOps is an evolving body of knowledge. There is no one-stop shop that covers all roles and the complete range of skills required for DevOps. A DevOps-competent workforce requires an ongoing commitment to DevOps skills development and mastery. Enterprises must approach DevOps training strategically to leverage training resources to their best advantage.

Learning Continuous Flow
The first way of DevOps, continuous flow, is the foundation for DevOps.

Why Is Learning Continuous Flow Important to Engineering DevOps?
A knowledgeable, skilled workforce is needed to meet strategic objectives. Without a solid understanding of DevOps concepts and terminology, communication between people and teams is inefficient and a source of bottlenecks and misunderstandings. Developing hands-on skills for the tools and workflows is needed to understand and implement DevOps pipelines. There is an industry-wide shortage of skilled DevOps candidates. New skills foster innovation. Knowledge workers reduce production costs and reduce mistakes. Having skilled coworkers makes for a satisfying working environment. Employee retention is better. Competitive advantages are derived from knowledge.

How Is Learning Continuous Flow Accomplished?
The following are recommended engineering practices for learning continuous flow.

People are encouraged to be continuous learners with an ongoing commitment to DevOps skills development. DevOps training is approached strategically to ensure DevOps training is well aligned to business goals. DevOps skills gap assessments are conducted and used to define and refine the training strategy. Leaders and cross-functional teams are encouraged to take DevOps fundamentals training. Leaders define a training strategy that best fits the needs and priorities of the organization to meet critical skills gaps.

The training strategy considers which skills and roles can be covered by inexpensive self-study resources. Determine which skills and roles warrant more costly training. DevOps training has a distinct budget. Compliance to DevOps training programs are tracked. DevOps leadership training is offered to management and staff that have a role relevant to DevOps. DevOps tools training includes the recommended engineering practices for using each DevOps tool in the Service Catalog. Staff are encouraged and supported to take DevOps certifications that are relevant to their assigned roles.

A DevOps training program is defined and includes a recommended curriculum for each role including leaders, developers, QA, Ops, and infrastructure and security staff that are operating in a DevOps environment. Recommended DevOps training resources are cataloged, and the catalog is visible for all stakeholders. Merit and recognition programs encourage employees to master DevOps skills.

All staff is allocated time for DevOps training. DevOps mentoring programs supplement formal training. Job swapping and immersive training is encouraged and planned. People are encouraged to attend external conferences and meetups and share what they learn with other team members.

What Is Needed to Implement Learning for Continuous Flow?
The following are recommended engineering practices for implementing a learning program for continuous flow.
Assign roles and responsibilities for DevOps training leadership (e.g., DevOps Sensei, Training PM, evangelist). Conduct a DevOps skills gap assessment. Define a training strategy that best addresses the gaps and fits the needs and priorities of the organization. Define a training budget that includes expenses and time allocations per role and skill.

Determine which skills and roles can be covered by inexpensive self-study resources. Determine which skills and roles warrant more costly commercial training. Identify training goals for each role. Ensure job recruiting profiles match skill requirements for DevOps skills that are most relevant to the organization strategic goals. Put in place the means to make training resources visible and available. Set training goals for teams and individuals. Put in place incentives and recognition programs for completion of training.

Some of the best sources of DevOps training resources are listed below:
1. The DevOps Institute
2. Partners of The DevOps Institute, such as Learning Tree International and Xellentro
3. Udemy
4. Recorded talks and publications available through IT Revolution
5. Books such as my own Engineering DevOps, by Marc Hornbeek.

What this Means
Organizations need a trained workforce to succeed with DevOps—even ‘The First Way’ of DevOps—continuous flow. Organizations need to approach DevOps training strategically to leverage DevOps training resources to their best advantage.

Technology is undoubtedly ruling over us lately. Owing to the COVID-19 pandemic, technology is our best bet today. At present, no industries are left untouched from adopting cutting-edge technologies like Artificial Intelligence (AI) and Machine Learning (ML). Some industries that adopted AI and ML include automotive industries, health, logistics, etc. The hospitality sector in India has also not been exempted from adopting such world-class technology to ensure profit maximization in the competitive market.

The hospitality industry encompasses a broad category of fields like tourism, lodging, transport, event planning, theme parks, and others. It is undoubtedly a wide sector, but notably, the leaders in the field seem to be the hotels and accommodation along with restaurants and bars. The hospitality sector in India strongly requires the much-needed application of AI and ML. It is not only for surviving in the pandemic scenario but also for maximizing their profit margins. This industry mostly caters to the wants of the customers more than their needs. Therefore, companies in the hospitality sector must adopt AI and ML for ensuring continuous delivery of their best services at a competitive price to their customers. Here’s how AI and ML help the hospitality sector in ensuring survival and profits.

Artificial intelligence enhances hotel operations

Answering customer calls, attending front desk guests while responding to online guest queries simultaneously is no more a troublesome issue that existed for so long. With Artificial Intelligence (AI), staff does not need to work under high-quality performance pressure and perform like robots which leads to poor service quality. Chatbots emerged as a saviour and a boon to assist a multitude of guests effortlessly. Chatbots take care of all the online queries from potential customers while the front desk staff can serve the guests in a hassle-free manner, thereby enhancing hotel operations without compromising on any customer.

AI boosts personalized services

AI personalization synthesizes diverse customer data sets to extract relevant insights. By leveraging the priceless insights, the hotels can target great marketing efforts and deliver outstanding services. This helps in offering a seamless customer experience. With AI, hotels can properly understand the preferences of each customer. AI chatbots also offer a personalized conversation that effectively addresses the individual needs of every customer more conveniently and helpfully. Customers prefer a fast response to all their queries, especially online. AI chatbots complement it by facilitating immediate response and managing multiple customer needs seamlessly.

Competitive pricing via ML

What’s important for every business to survive in the marketplace is to use a competitive pricing strategy. The hospitality sector leverages competitive pricing strategies that are considered to be one of the most significant strategies to attract customers and enhance sales without compromising on profits. What helps in achieving this is Machine Learning. It helps to achieve the best possible pricing for services based on hotel history, seasonability, local competition, external real-time events, local events, third-party promotions, etc. Basis such factors, the data is run via predictive models and can be analyzed to offer competitive pricing for companies that help them achieve an edge in the broad marketplace.

ML improves customer service and satisfaction

Customers belong from varied backgrounds so their expectations, needs, and demands differ from each other. A process known as Market Segmentation is employed in every sector, including the hospitality sector, to ensure that each customer’s wish is fulfilled according to their tastes and preferences. The entire customer spectrum is segregated into various segments with more or less similar expectations and demands. With Machine Learning (ML), the customer spectrum becomes more diverse into extremely smaller groups and sub-groups that were not witnessed ever before. This process improves the quality of customer service and satisfaction by ensuring a more profitable business.

Summing up

Both Artificial Intelligence and Machine Learning are leading the way to innovation in every sector. They continue to be explored and applied to new sectors every day. The hospitality sector should also leverage these technologies to tackle the unseen problems and challenges to satisfy the customers. Therefore, from enhancing a hotel’s operation, offering personalized services & competitive pricing to improving customer satisfaction via phenomenal service, the role of AI and ML to maximize profits in the hospitality sector is unparalleled. It shows us the untapped potential of the technologies that own the ability to change the face of the hospitality sector.

DevOps isn’t a new adventure for us at PTC. We first started our DevOps journey about four years ago with a team that was primarily focused on release management, software deployment, and automation of these two functions across the development team.

We started early, but I wouldn’t say we crossed the finish line with that first iteration. We incorporated some automation tools, software packaging, and release management, but it was more ad hoc and informal. We knew we needed to refocus our DevOps efforts, and have spent the last 18 months doing just that. 

Lessons from the second leg of our DevOps journey

Our focus with DevOps 2.0 has centered around CI/CD and continuous testing across all the scrum teams.

Our focus with DevOps 2.0 has centered around CI/CD and continuous testing across all the scrum teams in IT. The DevOps function has been really impactful in bringing a consistent approach across product lines and multiple technical platforms.

Today we’re a little more than halfway done with our DevOps 2.0 journey. We still have a lot of work to do, particularly around bringing all teams up to the same level of maturity to ensure a consistent level of performance, expectation, and velocity. We’ve made a lot of progress in our journey and have learned some valuable lessons along the way.

[ Get our free eBooks: DevOps Hiring: The Ultimate Guide and IT job searching in 2019: A practical guide. ]

Lesson 1: Prioritize change management

When we first dove into DevOps several years ago we underestimated the importance of change management across the various teams. Instead, our focus was misplaced on best practices, tools, and new processes.Focus on helping individuals and teams understand how their roles have changed, and what that means for them moving forward.

If I could go back and do things differently, I’d place a stronger focus on change management to help each of the teams and individuals understand how their role has changed. This would also include finding teams that are true champions and enlisting them to show other teams the path to success. It would mean identifying some early successes, finding out why some teams were more successful in adopting best practices, and promoting those ideas more so we could demonstrate the benefits across all the teams, ensuring that they’re all at the same level. 

This time around, we focused on exactly that – helping individuals and teams understand how their roles have changed, and what that means for them moving forward. 

Lesson 2: Do agile and DevOps concurrently

You can’t do agile before DevOps, and you can’t do DevOps before agile; they need to be done at the same time. Here’s why: Agile practices around development, like having product owners and scrum teams, will come to a screeching halt if CI/CD is not deployed fully.During our original foray into DevOps, we tried to focus on operations first, without the upstream development using agile.

You need to take both of those practices and move them along concurrently. They’re so tightly integrated that you can’t really separate them. During our original foray into DevOps, we tried to focus on operations first, without the upstream development using agile. That’s why there was a mismatch in collaboration with these teams, and collaboration is very, very key in order for DevOps to be successful.

Lesson 3: Consider geolocations

We discovered that teams that were farther away from the center of gravity — Boston, in our case — should have more focus on training and adoption. Otherwise, messaging gets diluted, which we found was a factor that impacted adoption. Team members who were farther away were less-immersed in the day-to-day processes than those who were geographically closer.

Lesson 4: Prioritize training

If you have multiple teams working on a single project and there’s a different level of maturity across this teams, the weakest team drags down the entire project. We learned this lesson the hard way. 

It wasn’t easy for us to figure out why velocity was slow, but we ultimately discovered that it was due in part to the differing levels of maturity across those teams. This time around, it was essential for us to make sure all teams were up to speed first. That’s where training came into play.

We had three focuses for training: First, we rolled agile into our journey and got it adopted across all teams via a consistent practice to ensure everyone was up to speed. Second, we ensured everyone was on the same page with CI/CD from both a best-practices and a tools standpoint. Finally, we looked at how their work changed with the adoption of these new practices. This highlighted how they did things before, and how they’d do things differently. 

What’s next 

When we relaunched our focus on DevOps, we did so deliberately, announcing the roles that were changing that would help us focus on this new way of doing things. There was a lot of communication across all teams, and we even went on a roadshow to talk about how we’d be deploying this and the benefits of relaunching it in a different way. 

I was also very transparent with my direct reports about areas in which we needed to improve. This was part of the culture I’ve developed at PTC – never minimizing or maximizing our successes or failures. We don’t need to mince words or soften things up: Let’s just admit it, learn from it, and move on. 

For others on this journey, it’s important to understand that every organization is different. You need to look at yours and your culture to determine how best to adopt DevOps to your organization. The same model might not work everywhere. 

Every IT organization needs to undertake this journey, however. It’s just too impactful not to. At PTC, for example, our speed of deployment has increased considerably. We used to do quarterly releases for some of our major applications and now we have a major release every month. 

Business demands are increasing and IT teams need to be more flexible while maintaining error-free control. The only way to do this is with a very deliberate process – the adoption of good DevOps practices and good agile practices. 

The resources below are free (or free-to-audit) and span beginner to intermediate levels. They cover core concepts such as pod scheduling, service discovery, persistent storage, role-based access control (RBAC), and multi-tier application deployments.

1. Architecting with Google Kubernetes Engine Specialisation (Coursera)

Offered by Google Cloud on Coursera, this specialisation focuses on practical implementation using Google Kubernetes Engine (GKE). Topics include building and scheduling workloads, load balancing, health monitoring, service discovery, RBAC and security configuration, and attaching persistent storage to applications. The course is auditable for free, although a certificate requires payment.

This is a strong choice if you are planning to run Kubernetes workloads specifically on Google Cloud, as it blends Kubernetes fundamentals with GKE-specific tooling and best practices.

2. Introduction to Kubernetes (edX / Linux Foundation)

Hosted on edX and produced by the Linux Foundation, this free-to-audit course provides a solid conceptual and hands-on foundation. You will learn:

• Core Kubernetes architecture (control plane, worker nodes, etcd, API server)
• How to set up a local cluster with Minikube and interact with it using kubectl
• Deploying stand-alone and multi-tier applications
• Working with ConfigMaps and Secrets for configuration management
• Configuring Ingress resources for external traffic routing

This course is well-suited to engineers who have some container experience (e.g., Docker) but are new to orchestration.

3. Learning Path: Kubernetes (IBM Developer)

IBM Developer’s Kubernetes learning path is a structured, multi-module curriculum that takes you from foundational prerequisites to more advanced topics. Key outcomes include:

• Understanding container fundamentals and why orchestration is necessary
• Building containerised applications and deploying them to a Kubernetes cluster
• Using Helm to manage application packaging and deployment
• Deploying microservices architectures on Kubernetes
• Understanding Kubernetes networking concepts (Services, DNS, ingress controllers)

The IBM Developer learning paths are self-paced and browser-based, making them easy to work through incrementally alongside a real project.

4. Learn Kubernetes Basics (Official Kubernetes Documentation)

The official Kubernetes interactive tutorial on kubernetes.io is the most authoritative starting point available. Divided into six modules, it walks you through:

• Creating a Kubernetes cluster
• Deploying a containerised application
• Exploring and debugging your app using kubectl
• Exposing the application publicly via a Service
• Scaling the deployment horizontally
• Performing a rolling update to deploy a new application version with zero downtime

Each module includes an embedded interactive terminal running a real Minikube cluster, so you do not need to install anything locally to complete the exercises. This is the ideal first stop for anyone brand new to Kubernetes.

5. Fundamentals of Containers, Kubernetes, and Red Hat OpenShift (edX)

Produced by Red Hat and hosted on edX, this course introduces container and orchestration technology through a practical lens, using Docker, Kubernetes, and the Red Hat OpenShift Container Platform. Learning objectives include:

• Containerising existing applications and services using Docker
• Managing container images and container lifecycles
• Building custom container images
• Deploying containerised applications onto an OpenShift cluster

This course is particularly relevant for teams working in enterprise environments where OpenShift is the preferred Kubernetes distribution, or for engineers who want to understand how OpenShift extends upstream Kubernetes.

6. A Tutorial Introduction to Kubernetes (Ulaş Türkmen’s Blog)

This community-contributed tutorial by Ulaş Türkmen provides a practical, hands-on introduction to running Kubernetes locally with Minikube. It covers:

• Configuring kubectl to communicate with your cluster
• Understanding nodes and namespaces
• Navigating the Kubernetes Dashboard
• Deploying container images and observing Kubernetes features in action
• Exposing deployments as Services

Community blog posts like this are often more concise and opinionated than official documentation, making them useful complements when you want a quick walkthrough without extensive background reading.

7. Introduction to Kubernetes (Linux Foundation — LFS158x)

The Linux Foundation’s standalone course LFS158x on edX covers Kubernetes from first principles with a focus on portability and open standards rather than any single cloud provider. Topics include:

• What container orchestration is and why it matters
• Kubernetes architecture and its major components
• Setting up and accessing a local cluster with Minikube
• Deploying stand-alone and multi-tier applications
• Kubernetes volume management and persistent storage concepts

Because the Linux Foundation is one of the stewards of the Kubernetes project itself, this course closely tracks upstream project developments and is regularly updated.

Choosing the Right Starting Point

If you are completely new to Kubernetes, begin with the official interactive tutorial on kubernetes.io (resource 4) to get hands-on experience without any local setup. Follow it with either the Linux Foundation edX course (resource 7) or the IBM Developer learning path (resource 3) for a more structured curriculum. Once you have the fundamentals, the GKE specialisation (resource 1) or the Red Hat OpenShift course (resource 5) will sharpen your skills for a specific platform environment.

All seven resources are free to access in audit mode or without registration, making them accessible regardless of budget. The investment required is time and practice — ideally alongside a real or local cluster.

This article breaks down the most significant recent additions to the AWS machine learning platform, organized by audience: infrastructure-focused developers and DevOps engineers who build and deploy custom models, and data scientists or non-specialists who need managed services to analyze large datasets without hand-rolling ML pipelines.

Who AWS Is Building For

AWS machine learning investments fall into two broad categories:

• Developer and DevOps tooling — Deep Learning AMIs and Deep Learning Containers target engineers building sophisticated, custom models and the teams responsible for deploying them on cloud infrastructure.
• Managed AI services — Services like Amazon Forecast and Amazon Personalize are designed for data scientists and non-specialists who want to apply advanced ML techniques to large datasets without managing the underlying infrastructure or writing training code from scratch.

AWS Deep Learning Containers

Setting up a reproducible environment for deep learning development has historically been a time-consuming task. Data scientists had to manually resolve dependencies across multiple source repositories, manage framework versions, and tune configuration for their target hardware — a largely DIY process prone to environment drift and hard-to-debug failures.

NVIDIA addressed this problem early with NVIDIA GPU Cloud (NGC), a registry of pre-built containers that bundle all the libraries and drivers needed to run deep learning workloads on NVIDIA hardware. AWS adopted a similar approach and introduced AWS Deep Learning Containers: a set of Docker images pre-configured with the most popular deep learning frameworks.

Supported Environments

AWS Deep Learning Containers can run across a range of AWS compute services and orchestration platforms:

• Amazon Elastic Kubernetes Service (EKS)
• Amazon Elastic Container Service (ECS)
• Amazon SageMaker
• Individual EC2 instances
• Self-managed Kubernetes clusters backed by EC2 instances

Frameworks and Runtime Support

At the time of this writing, the containers shipped with support for TensorFlow 1.14.0, MXNet 1.4.1, and the NVIDIA CUDA toolkit 10.0. Both frameworks expose Python APIs, which is the dominant scripting language in the data science community.

Key capabilities include:

• Model training and inference with both TensorFlow and MXNet.
• Distributed training with TensorFlow, supported via Horovod — an open-source distributed training framework originally developed by Uber.
• CPU and GPU instance compatibility on Ubuntu 16.04.
• SageMaker integration — useful for teams that want to run custom algorithms and containers without managing the training infrastructure directly.

Availability and Deployment

Deep Learning Containers are available at no additional charge from the AWS Marketplace or Amazon Elastic Container Registry (ECR). You pay only for the underlying compute resources you consume.

For Kubernetes-based deployments, the workflow is straightforward: create a pod specification file that references one of the Deep Learning Container images, then schedule it onto your cluster using kubectl. The same container images can be used consistently across local development, EKS, and SageMaker, reducing environment parity issues between development and production.

Deep Learning AMIs: Updates for Smaller-Scale Projects

For teams working on smaller projects — those involving a handful of VMs rather than a full container orchestration cluster — AWS maintains Deep Learning Amazon Machine Images (AMIs). These provide a similar pre-configured environment directly on EC2 instances, without the need to manage container infrastructure.

In 2019, AWS expanded framework support for Deep Learning AMIs with the following additions:

• PyTorch 1.1, Chainer 5.4, and CUDA 10 support for MXNet
• Amazon Linux 2 support, along with TensorFlow 1.13.1, MXNet 1.4.0, and Chainer 5.3.0

AWS also extended the Elastic Inference service to support TensorFlow 1.12, adding a new EIPredictor Python API. Elastic Inference allows you to attach a fractional GPU accelerator to any SageMaker or EC2 instance — rather than provisioning a full GPU instance — which can significantly reduce the cost of deep learning inference workloads while still improving throughput over CPU-only instances.

AWS AI Managed Services: Now Generally Available

Several AI services previewed at re:Invent 2018 reached general availability in the months that followed. These managed services abstract away model training, infrastructure provisioning, and hyperparameter tuning — users supply data, and AWS handles the ML pipeline.

Amazon Forecast

Amazon Forecast is a fully managed time series forecasting service built on technology Amazon developed for its own internal demand-planning use cases. Users supply historical time series data and any relevant metadata, and Forecast automatically selects and trains a predictive model. The service is designed to handle the kind of complex, multi-variable forecasting problems — such as product demand, resource utilization, or financial metrics — that are difficult to address with simple statistical methods.

Amazon Personalize

Amazon Personalize is a managed recommendation and personalization engine that draws on the same underlying models used in Amazon’s retail platform. Teams provide an interaction dataset — such as page views, purchases, or content engagement events — along with an item catalog. Personalize handles model training, tuning, and hosting, and returns ranked recommendations via an API. This makes it practical to add real-time personalization to applications without dedicated ML engineering resources.

Amazon ML Insights for QuickSight

ML Insights is an add-on module for Amazon QuickSight, AWS’s interactive business intelligence service. It introduces machine learning and natural language processing features directly into the BI workflow, including:

• Anomaly detection — automatically surfaces unexpected changes in metrics across large datasets.
• Forecasting — projects future values of time series data within QuickSight dashboards.
• Auto-narratives — generates plain-language textual descriptions of chart data, making it easier to communicate findings to non-technical stakeholders.

ML Insights is aimed at analysts who need more than standard statistical aggregation but do not want to build and maintain separate ML pipelines outside of their BI tooling.

Key Takeaways for DevOps and ML Teams

• If your team builds custom deep learning models, Deep Learning Containers remove the environment setup burden and work across EKS, ECS, SageMaker, and raw EC2.
• For smaller VM-based workloads, Deep Learning AMIs offer a faster path to a pre-configured training environment without container orchestration overhead.
• Elastic Inference is worth evaluating if inference cost on GPU instances is a concern — attaching fractional accelerators can reduce spend compared to full GPU instances.
• Managed services like Forecast and Personalize are production-ready options for common ML use cases where building and maintaining custom models is not justified by the available ML engineering capacity.

Source: Originally based on reporting from searchaws.techtarget.com (2019).

From the Back Room to the Boardroom

The shift did not happen overnight. Several converging forces lifted software development from a support function to a core business driver:

• Software is now the product. From e-commerce platforms and mobile apps to IoT devices, autonomous vehicles, and AI-powered assistants, the world runs on software. Every sector — finance, healthcare, logistics, retail — has discovered that its competitive advantage lives in code.
• IT-led innovation is boardroom priority. An Accenture survey of more than 1,200 business and technology leaders found that 90% cited IT-led innovation as critical to their company’s growth.
• Developer communities have become cultural events. Apple’s Worldwide Developers Conference (WWDC), now decades old, draws global media attention on par with major product launches, signalling how central developers have become to the technology industry’s public identity.

Job Growth: Developers Are in Demand

The numbers reflect the cultural shift. According to figures cited by The Knowledge Academy, more than 250,000 new developer roles were projected to exist by 2026 — more than any other job category in technology. The growth of artificial intelligence and machine learning has made Python expertise particularly sought-after, while the broader digitisation of every industry continues to drive demand across languages and platforms.

The trend is not limited to the United States. In Australia, government projections anticipated approximately 80,000 additional developer job openings over a five-year horizon, reflecting a global pattern rather than a regional anomaly.

Greater Power, Greater Responsibility

Increased influence has not come without cost. As developers gain autonomy and move closer to strategic decisions, they are expected to own far more of the software delivery lifecycle than earlier generations of engineers ever were.

Security is the clearest example. In traditional waterfall organisations, developers wrote code and handed it to a separate security team for review. That model is increasingly untenable. DevOps culture — and its natural evolution into DevSecOps — places security responsibility directly with the engineers who write the code. Developers must now consider threat modelling, dependency vulnerabilities, secrets management, and compliance requirements as part of their daily workflow, not as an afterthought at release time.

The Full-Stack Expectation and the Generalist Debate

Broader ownership of the software lifecycle gave rise to a controversial hiring archetype: the full-stack developer. As engineer and author Jeff Knupp observed, expanding developer responsibility created demand for a “chimera-like” candidate — someone capable of covering development, QA, operations, system administration, and database work simultaneously.

Knupp framed this partly as a negative consequence of agile startup thinking permeating larger organisations — a way to extract more labour from fewer headcount. There is validity to that critique. Expecting a single engineer to own an entire stack at the same depth a specialist would is often unrealistic and can lead to burnout.

The counterargument is that breadth of knowledge genuinely increases a developer’s ability to make better architectural decisions, communicate with adjacent teams, and ship more resilient software. The debate has not been settled, and the right answer varies significantly by company size and product complexity.

Lifelong Learning as a Career Strategy

Data from SlashData reports (as referenced in the original source) suggests that developers who invest in broadening their skillsets are responding rationally to market conditions. The areas attracting the most developer interest include:

• Cybersecurity — understanding how to build defensible systems, not just functional ones
• Cloud deployment — familiarity with AWS, Azure, and GCP is now close to a baseline expectation for many roles
• Data science and machine learning — growing demand for engineers who can work at the intersection of software and statistical modelling
• UI and UX design — product thinking and user empathy that improve what gets built, not just how it is built
• Project management and communication — soft skills that become increasingly important as developers move into senior and leadership positions

This upskilling dynamic is partly self-reinforcing. As developers expand their competencies, they earn greater organisational trust and influence, which in turn creates more opportunities to apply and develop those skills further.

The Limits of Developer Influence

It would be an overstatement to conclude that software developers are universally powerful across all organisational contexts. SlashData’s research points to a clear pattern: developer decision-making authority tends to diminish as company size increases. In large enterprises with established IT departments and traditional management hierarchies, developers may still find themselves relegated to execution roles, with strategy and architecture determined by committees or dedicated architecture teams far removed from day-to-day engineering.

Additionally, growth in team size naturally introduces process overhead. As organisations scale, roles specialise, and the individual developer’s direct influence over business direction is typically diluted across a larger organisational structure.

What This Means for DevOps Teams

For practitioners working in DevOps environments, the broader elevation of the developer role has practical consequences:

• Security ownership is non-negotiable. DevSecOps is not an optional add-on. Engineers are expected to understand the security implications of their design choices from the start.
• Operational awareness matters. Writing code that ships is table stakes. Writing code that is observable, resilient, and operable in production distinguishes senior engineers.
• Business context is a technical skill. Understanding how a feature drives revenue, user retention, or risk reduction makes engineers more effective collaborators and better at prioritisation.
• Specialism still has a place. Organisations need both generalists who can work across the stack and deep specialists in areas like database performance, network security, or compiler design. Healthy engineering teams include both.

Conclusion

The rise of the software developer from back-room technician to strategic contributor reflects a genuine and ongoing shift in how businesses create value. Code is no longer infrastructure — it is the product, the distribution channel, and often the business model itself. For developers willing to invest in continuous learning, broaden their understanding of security, operations, and business, and communicate effectively beyond their immediate team, the opportunity is substantial. The seat at the table is real, but it comes with an expanded job description.

Docker is everywhere in the software industry today. Mostly popular as a DevOps tool, Docker has stolen the hearts of many developers, system administrators and engineers, among others.

What is Docker?

“Docker is a tool that helps users to exploit operating-system-level virtualisation to develop and deliver software in packages called containers.

This technical definition may sound complicated, but all you need to know is that Docker is a complete environment where you can build and deploy software. It is just like your Linux machine, except that it is very lightweight, fast and has nothing except what you need for your project or software to run without a glitch.

One of the best things about Docker is that you can move it across platforms and still run without installing a single dependency — because all you need is a Docker Engine.

It gives developers, system administrators, DevOps people and many others the peace of mind of not having to worry about an application not working when moved to different platforms.

Now that we know what Docker is, let’s see how it is a big help in the machine learning spectrum.

Docker For ML Development

One of the simplest and most common uses of Docker not just in ML but across the entire development phase — it acts as a complete and solid development environment. We all know that setting up a development environment can be very messy at times, especially the fact that resolving dependencies can be a hectic task. But with Docker, it is as simple as a ‘Docker run’ command in most cases. In the worst-case scenario, we will have to build a custom Docker image with all the dependencies, with a little bit of shell programming knowledge it can be achieved in a matter of a few lines of script.

Docker file is an executable file that allows us to create our own custom Docker images. We can specify what the environment should consist of, all the dependencies and software that needs to be installed etc. After that, just execute a couple of commands and your development environment is ready to be spun up.

This would save a lot of time compared to trying to untangle the dependency errors caused by the different versions of applications in your system.

A good and simple example use case can be when you want to build a simple classifier,  and your system has a version of libraries and applications that are not updated and can not be updated since you have some applications that run on these versions. The easiest way to deal with it without messing up your current working set-up is by downloading or building a Docker Image with an environment that has the latest version of all the applications and libraries that you would need for building your new classifier or application.

Besides, many applications and software now come with official Docker Images which makes it effortless to install that application into a local machine.

Check out the official documentation to build a Docker image here.

Docker for deploying ML Applications

Another popular use case of Docker is in the deployment phase. Conventional deployment methods would require setting up the production environment to match the testing and development environments so that the application runs seamlessly without any trouble across all the instances. But this is not an easy-to-achieve task.

It is very much possible to miss some dependencies in any environment due to a number of reasons. In production, it might even be too late to realise that there have been missing dependencies.

With Docker, it’s all just a matter of executing a couple of commands to spin up a container. Docker containers can be shipped across platforms, the very reason they are called containers.It means that a program that runs on a Windows development server can never fail to run in a Linux production server. This adds a lot of flexibility in the development life cycle of an application.

If there is a new version of an application that uses new set of dependencies and has to be moved to production, just port the container of the new application to the production server and run it there, as easy as that, and what’s more impressive is, you can run any number of containers in a machine depending on its availability of resources. Which means you can deploy your new vision without interfering with the one already serving as production.

Docker For Distributed Computing Or Cloud

We are all pleased by the advantages offered by running a single container but when multiple Docker containers are combined they provide much more.

Clustering is a signature feature of Docker containers. Docker offers a solution to combine the collective power of containers called the Docker Swarm. Even Google’s Kubernetes is made for the same reason. This makes it possible to spin up containers across multiple machines or over the cloud and manage all of them collectively.

For ML application specifically, this can be a huge advantage since the application can be broken into modules and deployed across different machines all communicating with each other and managed by a single interface. This will add flexibility and scalability as more machines or containers can be added effortlessly.

Containers have built-in solutions that allow external and distributed data access which can be used to leverage common data-oriented interfaces that support many data models.

Docker With GPU

ML cannot be compared with a normal software development task. It is an extremely expensive task, both resource-wise and time-wise.

GPUs are one of the greatest power sources in High-Performance Computing (HPC). As one of the best GPU manufacturers and the leading one in providing HPC solutions for Deep Learning applications, NVIDIA has made a footprint, the CUDA framework, in the ML development space that many follow.

NVIDIA-Docker is a Docker solution for NVIDIA’s popular CUDA framework which can help in maximum resource utilization on the GPU. It provides a runtime that mounts the underlying  NVIDIA driver to a container which is totally independent of the version of CUDA that is installed in the machine. When run on a node GPU utilization becomes easy for Docker-containers thus enabling out resource greedy deep learning algorithms to run on large datasets without wasting any resources supplied by the GPU.

In A Nutshell

Docker is employed in various phases of ML development life cycle such as data gathering, data aggregation and preprocessing, data exploration, model training and predictive analysis, application deployment and more. It is a break-through technology that is not limited to Enterprise level usage or application. The community edition is free and one can easily set up and learn to use Docker. In fact, one of the easiest ways to set up a personal development environment is through Docker.