Preface

This book helps you acquire practical knowledge about machine learning experimentation on Azure. It covers everything you need to know and understand to become a certified Azure Data Scientist Associate.

The book starts with an introduction to data science, making sure you are familiar with the terminology used throughout the book. You then move into the Azure Machine Learning (AzureML) workspace, your working area for the rest of the book. You will discover the studio interface and manage the various components, like the data stores and the compute clusters.

You will then focus on no-code, and low-code experimentation. You will discover the Automated ML wizard, which helps you to locate and deploy optimal models for your dataset. You will also learn how to run end-to-end data science experiments using the designer provided in AzureML studio.

You will then deep dive into the code first data science experimentation. You will explore the AzureML Software Development Kit (SDK) for Python and learn how to create experiments and publish models using code. You will learn how to use powerful computer clusters to scale up and out your machine learning jobs. You will learn how to optimize your model’s hyperparameters using Hyperdrive. Then you will learn how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you will learn to operationalize it for batch or real-time inferences and how you can monitor it in production.

With this knowledge, you will have a good understanding of the Azure Machine Learning platform and you will be able to clear the DP100 exam with flying colors.