Understand and learn the skills needed to use modern tools in Microsoft Azure. This book discusses how to practically apply these tools in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity. It provides an end-to-end understanding of data science life cycle and the techniques to efficiently productionize workloads.
The book starts with an introduction to data science and discusses the statistical techniques data scientists should know. You'll then move on to machine learning in Azure where you will review the basics of data preparation and engineering, along with Azure ML service and automated machine learning. You'll also explore Azure Databricks and learn how to deploy, create and manage the same. In the final chapters you'll go through machine learning operations in Azure followed by the practical implementation of artificial intelligence through machine learning.
Data Science Solutions on Azure will reveal how the different Azure services work together using real life scenarios and how-to-build solutions in a single comprehensive cloud ecosystem.
What You'll Learn

Understand big data analytics with Spark in Azure Databricks
Integrate with Azure services like Azure Machine Learning and Azure Synaps
Deploy, publish and monitor your data science workloads with MLOps
Review data abstraction, model management and versioning with GitHub

Who This Book Is For
Data Scientists looking to deploy end-to-end solutions on Azure with latest tools and techniques.