Description
Course info
Level
Intermediate
Updated
May 26, 2020
Duration
1h 38m
Description

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.

About the author
About the author

Sahil Malik has been a Microsoft MVP for the past 8 years, author of several books and numerous articles in both the .NET and SharePoint space, consultant, and trainer who delivers talks at conferences internationally.

More from the author
More courses by Sahil Malik
Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
[Autogenerated] Hello. My name is Sile Malik and welcome to my course Microsoft Azure developer managing Microsoft Azure Air Solutions Companies and governments around the globe are pulling billions of dollars into a I. The projects are getting ever interesting and more complex. It is therefore natural to conclude that these products need management. In this course, I assert that an AI project is like any other software project, and the need to manage it with goods offer practices is more not less important with demos. I show you how you can use concepts such as Azure Seelye, the ml STK ml ops to fully automate your end to end process with examples and plenty of hands on demos. I show you how you can set up an azure developed pipeline to go from an experiment to a service. But the fun doesn't end there. I then show you how to deploy your model as an ache yes, 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 will have an in depth understanding of how to manage your AI products like a proper software project. Concepts such as ML apps and pipelines will be second nature to you, and you'll be a pro at collecting and monitoring your production. Ai Solutions data. I hope you find this course useful and thank you very much for watching.