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Machine Learning Operations (MLOps) is a combination of Machine Learning, DevOps, and Data Engineering. The role of MLOps is to deploy and maintain machine learning systems reliably and efficiently. 

The MLOps process consists of these three broad phases:

• Designing the ML-powered application
• ML Experimentation and Development
• ML Operations

MLOps is becoming a very popular career due to the increase in the use of machine learning algorithms in our everyday lives. With this, naturally the demand for MLOps engineers and related careers will also increase. This is where you may find yourself if you’re reading this article.

You may be considering a career in MLOps or have already decided to take the step. In this article, I will provide you with valuable learning resources from GitHub to help you become successful in your MLOps career.

MLOps Basics

Repository link: MLOps-Basics

If you’re new to MLOps, it would be good to start with the basics. Learning the foundations will allow you to understand deeper knowledge and allow you to apply your skills. This GitHub repo is a series, broken up into 9 weeks to aim to help you understand the basics of MLOps such as model building, monitoring, configurations, etc.

MLOps Guide

Repository link: mlops-guide

If you require a full walk-through of MLOps, this guide is for you. The aim of this guide is overall to help projects and companies to build a more reliable MLOps environment. 

It starts with the basics of MLOps, such as principles, architecture, etc. You can dive deeper into the theory behind MLOps and then move on to the implementation guide which will help you start your own project following a tutorial. 

Awesome MLOps 

Repository link: awesome-mlops

There’s never any harm in having too many options. This GitHub repository provides you with a curated list of references for MLOps. Regardless of your method of learning, if it’s via YouTube videos or articles - this repo has it all. 

You will have a list of resources to help you understand the core of MLOps along with communities that you can join. Other topic areas are Workflow Management, Feature Stores, Data Engineering, The Economics of ML/AI, and more. 

Awesome MLOps - Tools

Repository link: awesome-mlops

Another MLOps GitHub repository with the same name as the one above. However, this one is regarding the tools you will need to learn about MLOps. This will help you master different types of skills and be prepared for questions about them in the interview stages or when you land an MLOps job.

It covers tooling topics such as: 

• AutoML
• CI/CD for Machine Learning
• Data Exploration
• Data Management
• Data Processing
• Feature Engineering
• Hyperparameter Tuning
• Knowledge Sharing
• Machine Learning Platform
• , and more

DTU MLOps

Repository link: dtu_mlops

This GitHub repo is a Machine Learning Operations course provided by Danmarks Tekniske Universitet. In order to successfully go through this repo, there are prerequisites. You will need to have experience or knowledge of the following topics:

• General understanding of machine learning
• Basic knowledge of deep learning
• Coding in PyTorch

You will be provided with different types of exercises and valuable material to improve your understanding of machine learning operations

MLOps Course

Repository link: mlops-course

If you feel confident in your MLOps knowledge and skills, the next step is to put them to the test. The best way to do this is through project work. This GitHub repository provides you with a project-based course on the foundations of MLOps to responsibly develop, deploy and maintain ML.

It is a combination of machine learning with software engineering on how to build production-grade applications. This will help you build a solid portfolio and be able to prove yourself during the interview stage. 

Conclusion

There are readily available resources online to help you be successful in MLOps. It’s a matter of how much effort you’re willing to put into it, but it is definitely possible. 

If you need some guidance and structure to your learning roadmap, have a look at these: 

• The Complete MLOps Study Roadmap
• The Absolute Basics of MLOps

 
 
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.