Deploying and configuring AI solutions in Microsoft Azure to support business outcomes requires in-depth knowledge about the range of service and options available in Azure, as well as a foundational knowledge of what an AI solution actually is.
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.
James Bannan is a published author and experienced public speaker based in Melbourne, Australia. He is a Microsoft specialist, with a particular focus on Azure infrastructure architecture, development, and automation.
Course Overview Hi there, my name is James Bannan, and welcome to this Pluralsight course, Deploying AI Solutions in Microsoft Azure. I am a consultant and Azure architect based in Melbourne, Australia. I've authored a number of Pluralsight courses on Microsoft Azure, and helping customers migrate Azure, optimize their investments, and transform workloads is the main focus of what I do every day. This course will enable you to plan, deploy, and configure the different types of services and resources which make up AI solution architectures on Microsoft Azure, as well as gaining a thorough general understanding about the nature of AI solutions on Azure. Some of the major topics that we will cover include the role of continuous monitoring for AI solutions and how to configure resource diagnostics, understanding the various ways of running containers in Microsoft Azure and how to deploy solutions using both container instances and Kubernetes, and the purpose of running AI solutions in a distributed manner 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 deployments of these solutions to support a wide range of business and technical outcomes. Before beginning the course, you should already be familiar with Microsoft Azure. This is not an introductory course to Azure, so we are going to assume prior working knowledge about the platform, including understanding Microsoft Azure Resource Manager and Azure CLI. It will also help if you've had some prior exposure to Terraform, but this is not critical, and all of the template and script assets have been provided. I hope you'll join me on this journey to learn all about architecting and deploying the services which underpin AI solutions in this course, Deploying AI Solutions in Microsoft Azure, at Pluralsight.