What is the role of MLOps?

Are you interested in becoming an MLOps engineer? Learn more about the MLOps Engineer role today.

So a machine learning model has been built. It achieves the expected performance on the validation dataset. It was a pleasure to apply data science and machine learning skills to build this model. However, it's not very helpful to realize that the model runs fine (yet) on your local machine in a Jupyter notebook.

In order for users to benefit from the model and for the enterprise to take advantage of machine learning, the model must be deployed into a production environment. However, deploying and maintaining machine learning models is not without challenges. A model that performs well in a development environment may fail miserably in a production environment. This may be due to data and concept drift and other factors that cause performance degradation.

So realize: for machine learning models to be useful, you must go beyond model building. This is where MLOps come into play. Today, learn about MLOps and the role of an MLOps engineer in an organization.

What are MLOps?

Typically, you will find MLOps defined as applying DevOps principles to machine learning.

The software development life cycle (SDLC) has changed for the better as DevOps practices simplify cross-functional collaboration between development and operations teams. If you know someone who works in DevOps, you'll have heard them talk about CI/CD pipelines, automated CI/CD pipelines, application monitoring, and more.

Although this can be transferred to machine learning applications, there are some specific challenges with machine learning systems. Building and running machine learning machine learning systems is a more complex process.

So generally speaking, MLOps can be thought of as a set of best practices for building, deploying, and maintaining machine learning systems.

With that in mind, move on to understand what MLOps engineers do in your organization.

What does an MLOps engineer do?

DevOps practices can be applied to machine learning systems. If that's MLOps, then it's the MLOps engineer's responsibility to make it happen!

What does it mean? Once the data science team builds the model, MLOps engineers make the model run successfully by:

• Automated machine learning model deployment
• Set up monitoring for the ML pipeline
• Automate CI/CD pipelines to handle data, code, and model changes
• Set up automatic model retraining
• Determine the level of automation required

Some Challenges Related to MLOps

Setting up monitoring can only help identify when something goes wrong. To get more detailed information about the performance of different versions of a model, MLOps engineers often use model versioning and experiment tracking.

I\mentioned that MLOps engineers would set up model retraining with the desired level of automation. Try to understand the challenges associated with it.

Once the application is deployed to production, the data used by the model in production may be very different from the data it was trained on. As a result, such models will perform poorly and often have to be retrained.

MLOps engineers also address retraining and the automation of the retraining process by taking into account performance degradation, frequency of data changes, and model retraining costs.

In some startups, there may be machine learning engineers who also wear the hat of MLOps engineer. In other companies, there are DevOps and backend engineers who wear multiple hats.

The MLOps of a large tech company may be very different from the MLOps of an early-stage startup. The level of MLOps automation may also vary from organization to organization.

If you have been working at a startup and have ownership of the end-to-end machine learning pipeline (from model training to monitoring and maintaining the machine learning system), you are already an MLOps engineer.

Excited to explore the challenging role of MLOps Engineer? Summarize the skills required.

MLOps Skill Sets and Tools: Overview

MLOps engineers typically have strong ML, DevOps, and data engineering skills.

MLOps Engineer’s Role in the Organization

• Machine Learning Skills: Programming, working knowledge of machine learning algorithms and frameworks, and domain knowledge
• Software Engineering Skills: Querying and working with databases, testing ML models, Git and version control, frameworks such as FastAPI
• DevOps basics: Proficient in tools such as Docker and Kubernetes
• Experiment tracking: Familiar with experiment tracking frameworks such as MLflow
• Organization of data pipelines: Setup and automation using tools such as Prefect and Airflow Data pipeline
• Cloud infrastructure: Familiar with cloud infrastructure providers such as AWS and GCP and infrastructure-as-code tools (IaC) such as Terraform

Learn MLOps

If you are interested in learning more about MLOps, here is a list of resources to help you get started:

• DataTalks.Club’s MLOps Zoomcamp: DataTalks.Club's MLOps zoomcamp is a free course for learning all things MLOps - from model building to best practices for deployment and monitoring. You'll learn by building a project that brings everything you've learned together.
• MLOps Specialization on Coursera: DeepLearning.AI’s Machine Learning Engineering for Production (MLOps) Specialization. This four-course major teaches how to build production-grade machine learning systems.
• MLOps GitHub Repositories: A curated list of repositories for upgrading in MLOps.

Summary

In this article, we introduced the primary responsibilities of an MLOps engineer in an organization and the key MLOps skills.

As mentioned earlier, not all engineers who perform MLOps are called MLOps engineers. Also discussed is how the level of automation and reliance on actual day-to-day work in MLOps varies across organizations.

Like any other role, being a successful MLOps engineer requires soft skills such as effective communication, collaboration, and strategic problem solving. That said, if you want to try your hand at becoming an MLOps engineer, happy MLOps!

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