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.
You will learn:
- The value proposition for Machine Learning Operations (MLOps)
- How to integrate GitHub-based code with Azure ML via GitHub Actions
- How to deploy code changes and manage multiple Azure ML environments in a centralized manner