This course will set the scene for understanding the different Azure services which are available to enable and support AI solutions and how to map business requirements in order to arrive at the right recommendations for your data teams.
AI solutions in Microsoft Azure consist of a number of independent resources and services working together holistically to produce a complex solution. As an AI Engineer, In this course, Choosing the Appropriate Microsoft Azure Services and Features, you’ll gain understanding of the processes which are inherent within an AI solution as well as some of the challenges faced by data scientists in the building and training of machine learning models in order to be able to recommend the most appropriate services and features. First, you’ll look at the different types of data that you’re going to need to understand and deal with, including recommending the right services to support different data scenarios. Then, you’ll explore the difference between machine learning models and frameworks, including the different types of machine learning models, incorporating both standard machine learning as well as deep learning. Finally, you’ll explore machine learning pipelines and automated machine learning, including the business and technical challenges that these technologies are designed to overcome. At the end of this course, you will have a basic understanding of the different Azure services.
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, Choosing the Appropriate Services and Features in Microsoft Azure. This course is part of the Azure AI engineer learning path, and everything we will cover in this course is focused on understanding the services which support AI solutions running in Microsoft Azure. I am a consultant in Azure Architects, based in Melbourne, Australia. I've authored a number of Pluralsight courses on Microsoft Azure, and helping customers migrate to Azure, optimize investments, and transform workloads is the main focus of what I do every day. This course will enable you to understand the types of problems and decisions involved in architecting and supporting the services, which in turn support AI solutions in Microsoft Azure, as well as gaining a foundational understanding about the role of machine learning models and frameworks, including some guiding principles of data science. Some of the major topics that we will cover include the types of data which you're likely to encounter when dealing with AI and machine learning solutions, and how to choose the right services to support them; the different types of machine learning models which data scientists will need to make use of; as well as the frameworks which are supported on Azure to accelerate the development and training of custom models; and finally, the role of Azure Cognitive Services and Azure Machine Learning Services in the overall architecture of AI solutions. By the end of this course, you'll have a thorough understanding of the different types of services available in Microsoft Azure to support AI solutions, and how you as an AI engineer can influence the decision‑making process to choose the most appropriate solutions based on business and technical requirements. Before beginning the course, you should already be familiar with Microsoft Azure. This is not an introductory course to Azure, so we're going to assume prior working knowledge about the platform, including understanding of Microsoft Azure Resource Manager. I hope you'll join me on this journey to learn all about choosing the Azure services and features which supports AI solutions, in this course, Choosing the Appropriate Services and Features in Microsoft Azure, at Pluralsight.