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


In this chapter, we learned when and how to use DL to train an ML model on Azure. We used both a compute instance and a GPU cluster from within the Azure Machine Learning service to train a model using Keras and TensorFlow.

First, we found out that DL works very well on highly structured data with non-obvious relationships from the raw input data to the resulting prediction. Good examples include image classification, speech-to-text, and translation. We also saw that DL models are parametric models with a large number of parameters, so we often need a large amount of labeled or augmented input data. In contrast to traditional ML approaches, the extra parameters are used to train a fully end-to-end model, also including feature extraction from the raw input data.

Training a CNN using the Azure Machine Learning service is not difficult. We saw many approaches, from prototyping in Jupyter to augmenting the training data, to running the training on a GPU cluster with autoscaling...