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)
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

Chapter 11: Hyperparameter Tuning and Automated Machine Learning

In the previous chapter, we learned how to train convolutional neural networks and complex deep neural networks. When training these models, we are often confronted with difficult choices in terms of the various parameters we should use, such as the number of layers, filter dimensions, the type and order of layers, regularization, batch size, learning rate, the number of epochs, and many more. And this is not only the case for DNNs – the same challenges arise when we need to select the correct preprocessing steps, features, models, and model parameters in statistical ML approaches.

In this chapter, we will look at optimizing the training process to remove some of the non-optimal human choices in ML. This will help you train better models faster and more efficiently without manual intervention. First, we will explore hyperparameter optimization (also called HyperDrive in Azure Machine Learning), a standard technique...