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

Building and publishing an ML pipeline

Let's go ahead and use all we have learned from the previous chapters and build a pipeline for data processing. We will use the Azure Machine Learning SDK for Python to define all the pipeline steps as Python code so that it can be easily managed, reviewed, and checked into version control as an authoring script.

We will define a pipeline as a linear sequence of pipeline steps. Each step will have an input and output defined as pipeline data sinks and sources. Each step will be associated with a compute target that defines both the execution environment as well as the compute resource for execution. We will set up an execution environment as a Docker container with all the required Python libraries and run the pipeline steps on a training cluster in Azure Machine Learning.

A pipeline runs as an experiment in your Azure Machine Learning workspace. We can either submit the pipeline as part of the authoring script, deploy it as a web service...