Managing Microsoft Azure AI Solutions
This course shows you how to manage AI solutions in Azure. It explains how you can use ML Ops, monitor and collect data in production AKS clusters, and automate the entire process end to end
What you'll learn
Companies and Governments across the globe are pouring billions of dollars into AI. The projects are getting ever more interesting and complex, and it is therefore natural to conclude that these projects need management.
In this course, Managing Microsoft Azure AI Solutions, I assert that an AI project is like any other software project, and the need to manage it with good software practices is more, not less, important. With demos, you'll learn how you can use concepts such as Azure CLI, ML SDK, and ML Ops to fully automate your end to end process. You'll also explore how you can set up an Azure DevOps pipeline to go from experiment to a service. But the fun doesn't end there; you'll then discover how to deploy your model as an AKS cluster and enable data monitoring and collection in production, so you can use that data in numerous ways to analyze it or feed it back into your model for subsequent improvement.
By the end of this course, you'll have an in-depth understanding of how to manage your AI projects like a proper software project. Concepts such as ML Ops and Pipelines will be second nature to you, and you'll be a pro at collecting and monitoring your production AI solutions.
Table of contents
- Introduction 1m
- The Role of DevOps 3m
- Important Components 2m
- Prerequisites and Setup 1m
- Setup Azure CLI and ML Extensions 3m
- Setup DevOps and Source Code 3m
- Install the Machine Learning Extension 2m
- Create an ML Workspace 2m
- Train and Register a Model 1m
- Train and Register a Model Using Azure CLI 13m
- Train and Register a Model Using DevOps Pipelines 9m
- Summary 1m