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)
1
Section 1: Introduction to Azure Machine Learning
5
Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
11
Section 3: The Training and Optimization of Machine Learning Models
17
Section 4: Machine Learning Model Deployment and Operations

Choosing an Azure service for ML

Azure provides more than 200 services, of which more than 30 services are targeted for building solutions for AI and ML. This vast number of services often makes it difficult for someone new to Azure to choose the right service for a specific task. Choosing the right service for your ML task is the most important decision you will have to make when starting with ML in Azure. In this section, we will provide clear guidance about how to choose the right ML and compute services in Azure.

The right service with the right layer of abstraction could save you months if not years of time to market your ML-based product or feature. It could help you avoid tedious time-consuming tasks such as improving model performance through transfer learning, re-training, managing, and re-deploying ML models, or monitoring, scaling, and operating inference services and endpoints.

Choosing the wrong service could mean that you start producing results quickly, but it...