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.
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.
