Book Image

Azure Data and AI Architect Handbook

By : Olivier Mertens, Breght Van Baelen
Book Image

Azure Data and AI Architect Handbook

By: Olivier Mertens, Breght Van Baelen

Overview of this book

With data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions. By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.
Table of Contents (18 chapters)
1
Part 1: Introduction to Azure Data Architect
4
Part 2: Data Engineering on Azure
8
Part 3: Data Warehousing and Analytics
13
Part 4: Data Security, Governance, and Compliance

Summary

This chapter focused on data science and AI on Azure. We started by outlining the different roles involved in a data science team, including the responsibilities of data architects, engineers, scientists, and machine learning engineers, and how the collaboration between these roles is key to building successful AI solutions.

We then focused on the role of the data architect when designing an AI solution, outlining the questions they should ask themselves for a well-architected design.

Next, we delved into the various AI services offered by Azure, including Azure Cognitive Services, Azure Machine Learning, and Azure OpenAI Service. For Azure Cognitive Services, we saw speech, vision, language, and decision-making models. For Azure OpenAI Service, we explored the GPT model family, Codex, and DALL-E 2.

We then introduced the concept of MLOps, along with mapping the different steps in the MLOps process to components with Azure Machine Learning, the workspace used for custom...