Book Image

Mastering Azure Machine Learning - Second Edition

By : Körner, Alsdorf
Book Image

Mastering Azure Machine Learning - Second Edition

By: Körner, 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

Using datasets in Azure Machine Learning

In the previous sections of this chapter, we discussed how to get data into the cloud, store the data in a datastore, and connect the data via a datastore and dataset to an Azure Machine Learning workspace. We did all this effort of managing the data and data access centrally in order to use the data across all compute environments, either for experimentation, training, or inferencing. In this section, we will focus on how to create, explore, and access these datasets during training.

Once the data is managed as datasets, we can track the data that was used for each experimentation or training run in Azure Machine Learning. This will give us visibility of the data used for a specific training run and for the trained model – an essential step in creating reproducible end-to-end machine learning workflows.

Another benefit of organizing your data into datasets is that you can easily pass a managed dataset to your experimentation or...