Beskrivning

Machine learning operations (MLOps) applies DevOps principles to machine learning projects. In this course you will learn about which DevOps principles help in scaling a machine learning project from experimentation to production.

You need to have some familiarity with machine learning and Azure Machine Learning to get the best results from this training. This course is also highly recommended as a group workshop for more from the same employer to get the best view on how you should work with MLOps in the future.

1. Introduction to DevOps principles for machine Learning

Get familiar with DevOps principles and tools relevant for MLOps workloads.

In this module, you’ll learn:

Why DevOps is useful for machine learning projects.
Which DevOps principles can be applied to machine learning projects.
How to connect Azure DevOps and GitHub with Azure Machine Learning.

2. Source control for machine learning Projects

Learn how to work with source control for your machine learning projects. Source control is an essential part of machine learning operations (MLOps).

Learning objectives

Trunk-based development with Git.
How to work with Git in Azure Repos and GitHub.
How to develop locally with Visual Studio Code.

3. Automate machine learning workflows

Automate machine learning workflows with Azure Machine Learning pipelines, Azure Pipelines, and GitHub Actions.

Learning objectives

How to use Azure Machine Learning pipelines.
How to use Azure Pipelines and GitHub Actions to automate workflows.

4. Continuous deployment for machine Learning

Learn how to work with environments for continuous deployment of machine learning models.

Learning objectives

How to set up environments for development and production.
How to control deployments with approval gates.

Kursen levereras i samarbete med Microsoft