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

Choosing data storage solutions for Azure Machine Learning

When running ML experiments or training scripts on your local development machine, you often don't think about managing your datasets. You probably store your training data on your local hard drive, external storage device, or file share. In such a case, accessing the data for experimentation or training is not a problem, and you don't have to worry about the data location, access permissions, maximal throughput, parallel access, storage and egress cost, data versioning, and such.

However, as soon as you start training an ML model on remote compute targets, such as a VM in the cloud or within Azure Machine Learning, you must make sure that all your executables can access the training data efficiently. This is even more relevant if you collaborate with other people who also need to access the data in parallel for experimentation, labeling, and training from multiple environments and multiple machines. And if you...