Book Image

Mastering Azure Machine Learning - Second Edition

By : Körner, Alsdorf
Book Image

Mastering Azure Machine Learning - Second Edition

By: Körner, 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

Summary

In this chapter, we introduced MLOps, a DevOps-like workflow for developing, deploying, and operating ML services. DevOps stands for a quick and high-quality way of making changes to code and deploying these changes to production.

We first learned that Azure DevOps gives us all the features to run powerful CI/CD pipelines. We can run either build pipelines, where steps are coded in YAML, or release pipelines, which are configured in the UI. Release pipelines can have manual or multiple automatic triggers (for example, a commit in the version control repository or if the artifact of a model registry was updated) and create an output artifact for release or deployment.

Version-controlling your code is necessary, but it's not enough to run proper CI/CD pipelines. In order to create reproducible builds, we need to make sure that the dataset is also versioned and pseudo-random generators are seeded with a specified parameter. Environments and infrastructure should also...