MLOPS For DevOps Engineers

👋 Hi! I’m Bibin Wilson. In each edition, I share practical tips, guides, and the latest trends in DevOps and MLOps to make your day-to-day DevOps tasks more efficient. If someone forwarded this email to you, you can subscribe here to never miss out!

In this edition, we will look at what MLOps means for DevOps engineers and how it fits into their workflow

My MLOPS Experience

In 2020, I worked on an ML project for an international sports brand, setting up the infrastructure from development to production.

At first, I thought it was just another automation task, something I had done countless times before.

But everything changed when I started attending ML team meetings. I had no clue what they were talking about. The discussions felt like a foreign language. Models, Feature engineering, training, hyperparameters etc. I was completely lost.

It hit me then: This wasn’t just about setting up pipelines.

It was like building CI/CD for a Java app without understanding how Java development works. If you don’t know how the code is built, optimized, and deployed, how can you design the right workflows? The same applies to ML.

I realized that to do my job well, I needed to understand ML itself.

So, I spent the next couple of months learning, picking up the basics, understanding the workflows, and getting familiar with the process.

I could finally follow discussions, collaborate better, and build infrastructure that actually made sense for the ML team.

DevOps to MLOPS

First of all, there is no such thing as switching careers from DevOps to MLOps. It's Not a Career Switch. MLOps is an extension of DevOps, not a separate career path.

Whoever says that probably has no clue what they are talking about.

Because DevOps engineers are already part of an ML project, which includes other teams like Data Science, Data Engineers, ML Developers, etc.

Also, you will be doing similar DevOps work, like setting up CI/CD, writing infrastructure as code, Python scripts, etc.

The only difference is in the tools, platforms, technologies, workflows, and how we create pipelines.

In short, you can say you are a DevOps engineer with expertise in managing Machine Learning operations. Because, at the core, you will still be following DevOps principles, which focus on collaborating with different teams efficiently.

So what is MLOPS really?

MLOps (Machine Learning Operations) is the practice of streamlining and automating the lifecycle of machine learning models, from development to deployment and maintenance.

So how is it different from usual DevOps workflows?

MLOps is different from the usual DevOps workflows, just like machine learning is different from traditional software development.

For example,

In MLOps, you need to track three components: code, data, and models (while in traditional DevOps, you only track code).

Skills Required For DevOps Engineers

DevOps engineers do not need to know how to build ML models or understand complex ML algorithms.

But for a DevOps engineer to efficiently collaborate, build, and maintain ML infrastructure, he/she should have an understanding of the following:

1. Understanding basic ML terminology and concepts: For example, ML model training, inference, what model artifacts are, etc.

2. ML Workflow Understanding: How ML experiments work, why models need retraining, what model drift means, etc.

3. ML Infrastructure Requirements: Computing resource needs for training, storage requirements for datasets, model serving patterns, GPU vs CPU requirements.

The list is not limited to the above. There is more. But you get the gist.

Once you have a good understanding of ML basics, you will be able to have effective conversations with data scientists, design appropriate infrastructure, set up proper monitoring, developer-centric workflows, CI/CD pipelines, etc., as you would normally do in a DevOps project.

Continued Tomorrow

This is just the beginning!

There’s a lot more to explore in MLOps, and we are going to break it down step by step.

Stay tuned for the next edition, where we dive deeper into the world of MLOps – the DevOps way!