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 16: Bringing Models into Production with MLOps

In the previous chapter, we looked into model interoperability using ONNX, hardware optimization using FPGAs, and the integration of trained models into other services and platforms. So far, you have learned how to implement each step in an end-to-end machine learning pipeline with data cleansing, preprocessing, labeling, experimentation, model training, optimization, and deployment. In this chapter, we will connect the bits and pieces from all the previous chapters to integrate and automate them in a build and release pipeline. We will reuse all these concepts to build a version-controlled, reproducible, automated ML training and deployment process as a continuous integration and continuous deployment (CI/CD) pipeline in Azure. In analogy to the DevOps methodology in software development, we will refer to this topic as MLOps in ML.

First, we will take a look at how to produce reproducible builds, environments, and deployments...