Deploying and Managing Microsoft Azure AI Solutions


Deploying and Managing Microsoft Azure AI Solutions

Authors: James Bannan, Sahil Malik, Ranjan Relan

Microsoft Azure offers a robust and powerful AI platform. Part of this platform helps you deploy, monitor, and manage your AI solution. This path addresses the operational... Read more

What you will learn:

  • Manage and validate deployment and configuration of Microsoft Azure AI services
  • Manage security artifacts
  • Monitor and manage models in production
  • Optimize every element of your AI solution


This skill path is intended for learners with experience designing AI solutions, but who are novice with regards to operationalizing them inside of Microsoft Azure. Prior knowledge of machine learning principles, the data science workflow, and Microsoft Azure is expected.


The courses in this section discuss provisioning of services and storage for your AI solution, as well as continuous integration.

Microsoft Azure AI Engineer: Deploying AI Solutions in Microsoft Azure

by James Bannan

Dec 13, 2019 / 2h 20m

2h 20m

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AI solutions in Microsoft Azure consist of a number of independent resources and services working together holistically to produce a complex solution. In this course, Microsoft Azure AI Engineer: Deploying AI Solutions in Microsoft Azure, you’ll gain an understanding of the nature of AI solutions in order to be able to recommend the right services and configuration options which will enable the work of data scientists and ensure that business investment in AI is both fit-for-purpose and well maintained. First, you’ll look at the core nature of AI solutions and how these are represented by solution architectures in Microsoft Azure. Then, you’ll work on the different scenarios to provide the computing services which data scientists need to build, train, and deploy AI models, including understanding the operational frameworks of monitoring, security, and zero-trust architecture. Finally, you’ll bring everything together by deploying an AI model in a container-based solution using IoT Edge. By the end of this course, you’ll have a thorough understanding of how AI solutions in Microsoft Azure are put together, and how you, as an AI Engineer, can influence the architecture, configuration, and deployment of these solutions to support a wide range of business and technical outcomes.

Table of contents
  1. Course Overview
  2. Deploying Azure AI Solutions
  3. Understanding Continuous Monitoring
  4. Deploying Container Environments
  5. Deploying IoT Devices
  6. Managing Security for AI Solutions


In this section, you will learn how to monitor the AI solution environment, review costs and performance metrics of your deployed solution, and updating and replacing models in production.

Managing Microsoft Azure AI Solutions

by Sahil Malik

May 26, 2020 / 1h 38m

1h 38m

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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
  1. Course Overview
  2. Managing Models in Azure Machine Learning Service
  3. Registering Your Model
  4. Registering and Deploying Your Image
  5. Monitoring and Improving Data and Models
  6. Wrapping Up


This section covers AI solution optimization, including effective use of storage, core services, and automation.

Optimizing Microsoft Azure AI Solutions

by Ranjan Relan

Sep 14, 2020 / 1h 26m

1h 26m

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Cloud-based platform Microsoft Azure has multiple AI services which could be used to train your model for big data sets as well as to deploy your model as a web service. In this course, Optimizing Microsoft Azure AI Solutions, you will learn the foundational knowledge of how to train your machine learning models using Azure's services such AzureML Compute Cluster, Azure HDInsight, Azure Databricks, and Azure Data Science Virtual Machine. Next, you will discover how to optimize your storage by using Azure Premium blob storage service and data formats such as Pickle and Parquet. Finally, you will explore how to scale your machine learning models and manage end-to-end machine learning life cycle using the principle of MLOps. When you’re finished with this course, you will have the skills and knowledge of Mircosoft Azure's core AI services needed to design, deploy, and optimize your model.

Table of contents
  1. Course Overview
  2. Optimizing Core Services
  3. Optimizing Storage and Logging
  4. Optimizing Deployments and Operations
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