Practical MLOps with GitHub and Azure ML

ABSTRACT

Azure Machine Learning provides extensive capabilities for low-code and full-code machine learning tasks. Once you have a full-code solution in place, the next step is to take advantage of Machine Learning Operations (MLOps). In this talk, we will gain an understanding of what MLOps is and why it is valuable for data science teams to follow this maturity model. We will use GitHub Actions as an example of how to maintain machine learning code and deploy it in a practical way.

ADDITIONAL MEDIA

I have created a playlist for Azure Machine Learning YouTube videos. These include most of the content of this talk series, as well as some additional notes.

SLIDES

Click here to access the slides for this presentation.

The slides are licensed under Creative Commons Attribution-ShareAlike.

DEMO CODE

Click here to access demo code for this presentation.

The source code is licensed under the terms offered by the GPL.

LINKS & FURTHER INFO

Helpful Resources

• The Microsoft MLOps repo
• The reference architecture used in the above MLOps demo
• Getting started with MLOpsPython
• Google's whitepaper on MLOps
• Microsoft's whitepaper on MLOps
• Azure ML examples repo
• Use GitHub Actions with Azure ML
• Use GitHub Actions to connect to Azure
• Components of GitHub Actions
• Events that trigger GitHub Actions workflows
• Branching models for Git repos
• Cronmaker: create cron schedules