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

Chapter 2: Choosing the Right Machine Learning Service in Azure

In the previous chapter, we learned about the end-to-end ML process and all the required steps, from data exploration to data preprocessing, training, optimization, deployment, and operation. Understanding the whole process will better help us in choosing the right service for building cloud-based ML services.

In this chapter, we will help you navigate the different Azure AI services and show you how to select the right service for your ML task. First, we will classify the different services by service abstraction and application domain, and then look at the different trade-offs and benefits of the different services.

In the next section, we will focus on managed services and jump right into Azure Cognitive Services, multiple pre-trained ML services for general tasks and domains. We will then cover customized Cognitive Services, which is a way to fine-tune a Cognitive Service for a specific task or domain, and end...