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 ForData Scientists looking to deploy end-to-end
solutions on Azure with latest tools and techniques.
