Book Image

Mastering Azure Machine Learning - Second Edition

By : Christoph Körner, Marcel Alsdorf
Book Image

Mastering Azure Machine Learning - Second Edition

By: Christoph Körner, Marcel Alsdorf

Overview of this book

Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you’ll be able to combine all the steps you’ve learned by building an MLOps pipeline.
Table of Contents (23 chapters)
Section 1: Introduction to Azure Machine Learning
Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
Section 3: The Training and Optimization of Machine Learning Models
Section 4: Machine Learning Model Deployment and Operations

Section 1: Introduction to Azure Machine Learning

In this section, we will learn about the history of Machine Learning (ML), the scenarios in which to apply ML, the statistical knowledge necessary, and the steps and components required for running a custom end-to-end ML project. We will have a look at the available Azure services for ML and we will learn about the scenarios they are best suited for. Finally, we will introduce Azure Machine Learning, the main service we will utilize throughout the rest of the book. We will understand how to deploy this service and use it to run our first ML experiments in the cloud.

This section comprises the following chapters:

  • Chapter 1, Understanding the End-to-End Machine Learning Process
  • Chapter 2, Choosing the Right Machine Learning Service in Azure
  • Chapter 3, Preparing the Azure Machine Learning Workspace