What this book covers
Chapter 1, An Overview of Modern Data Science, provides you with the terminology used throughout the book.
Chapter 2, Deploying Azure Machine Learning Workspace Resources, helps you understand the deployment options for an Azure Machine Learning (AzureML) workspace.
Chapter 3, Azure Machine Learning Studio Components, provides an overview of the studio web interface you will be using to conduct your data science experiments.
Chapter 4, Configuring the Workspace, helps you understand how to provision computational resources and connect to data sources that host your datasets.
Chapter 5, Letting the Machines Do the Model Training, guides you on your first Automated Machine Learning (AutoML) experiment and how to deploy the best-trained model as a web endpoint through the studio’s wizards.
Chapter 6, Visual Model Training and Publishing, helps you author a training pipeline through the studio’s designer experience. You will learn how to operationalize the trained model through a batch or a real-time pipeline by promoting the trained pipeline within the designer.
Chapter 7, The AzureML Python SDK, gets you started on the code-first data science experimentation. You will understand how the AzureML Python SDK is structured, and you will learn how to manage AzureML resources like compute clusters with code.
Chapter 8, Experimenting with Python Code, helps you train your first machine learning model with code. It guides you on how to track model metrics and scale-out your training efforts to bigger compute clusters.
Chapter 9, Optimizing the ML Model, shows you how to optimize your machine learning model with Hyperparameter tuning and helps you discover the best model for your dataset by kicking off an AutoML experiment with code.
Chapter 10, Understanding Model Results, introduces you to the concept of responsible AI and deep dives into the tools that allow you to interpret your models’ predictions, analyze the errors that your models are prone to, and detect potential fairness issues.
Chapter 11, Working with Pipelines, guides you on authoring repeatable processes by defining multi-step pipelines using the AzureML Python SDK.
Chapter 12, Operationalizing Models with Code, helps you register your trained models and operationalize them through real-time web endpoints or batch parallel processing pipelines.