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 10: Training Deep Neural Networks on Azure

In the previous chapter, we learned how to train and score classical ML models using non-parametric tree-based ensemble methods. While these methods work well on many small- and medium-sized datasets that contain categorical variables, they don't generalize well on large datasets.

In this chapter, we will train complex parametric models using deep learning (DL) for even better generalization with very large datasets. This will help you understand deep neural networks (DNNs), how to train and use them, and when they perform better than traditional models.

First, we will provide a short and practical overview of why and when DL works well and focus on understanding the general principles and rationale rather than the theoretical approach. This will help you to assess which use cases and datasets need DL and how it works in general.

Then, we will look at one of the popular application domains for DL – computer vision...