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

Understanding categorical data

Categorical data comes in many forms, shapes, and meanings. It is extremely important to understand what type of data you are dealing with—is it a string, text, or numeric value disguised as a categorical value? This information is essential for data preprocessing, feature extraction, and model selection.

In this section, first, we will take a look at the different types of categorical data—namely ordinal, nominal, and text. Depending on the type, you can use different methods to extract information or other valuable data from it. Please bear in mind that categorical data is ubiquitous, whether it is in an ID column, a nominal category, an ordinal category, or a free-text field. It's worth mentioning that the more information you have on the data, the easier the preprocessing is.

Next, we will actually preprocess the ordinal and nominal categorical data by transforming it into numerical values. This is a required step when you...