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 14: Model Deployment, Endpoints, and Operations

In the previous chapter, we learned how to build efficient and scalable recommender engines through feature engineering, natural language processing, and distributed algorithms.

In this chapter, we will tackle the next step after training a recommender engine or any machine learning model; we are going to deploy and operate the ML model. This will require us to package and register the model, build an execution runtime, build a web service, and deploy all components to an execution target.

First, we will take a look at all the required preparations to deploy ML models to production. You will learn the steps that are required in a typical deployment process, how to package and register trained models, how to define and build inferencing environments, and how to choose a deployment target to run the model.

In the next section, we will learn how to build a web service for a real-time scoring service, similar to Azure Cognitive...