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

Using pipelines in ML workflows

Separating your workflow into reusable configurable steps and combining these steps into an end-to-end pipeline provides many benefits for implementing end-to-end ML processes. Multiple teams can own and iterate on individual steps to improve the pipeline over time, while others can easily integrate each version of the pipeline into their current setup.

The pipeline itself doesn't only split code from execution; it also splits the execution from orchestration. Hence, you can configure individual compute targets that can be used to optimize your execution and provide parallel execution while you don't have to touch the ML code.

We will take a quick look at Azure Machine Learning pipelines and why they are your tool of choice when implementing ML workflows in Azure. In the following section, Building and publishing an ML pipeline, we will dive a lot deeper and explore the individual features by building such a pipeline.

Why build pipelines...