Book Image

Mastering Azure Machine Learning

By : Christoph Körner, Kaijisse Waaijer
Book Image

Mastering Azure Machine Learning

By: Christoph Körner, Kaijisse Waaijer

Overview of this book

The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure.
Table of Contents (20 chapters)
1
Section 1: Azure Machine Learning
4
Section 2: Experimentation and Data Preparation
9
Section 3: Training Machine Learning Models
15
Section 4: Optimization and Deployment of Machine Learning Models
19
Index

5. Azure Machine Learning pipelines

In the previous chapters, we learned about many extract, transform, and load (ETL) preprocessing and feature-engineering approaches within the Azure Machine Learning using Dataset, Datastore, and DataPrep. In this chapter, you will learn how to use these transformation techniques to build reusable machine learning (ML) pipelines.

First, you will learn about the benefits of splitting your code into individual steps and wrapping them into a pipeline. Not only can you make your code blocks reusable through modularization and parameters, but you can also control the compute targets for individual steps. This helps to optimally scale your computations, save costs, and improve performance at the same time. Lastly, you can parameterize and trigger your pipelines through an HTTP endpoint or through a recurring or reactive schedule.

After that, we'll build a complex Azure Machine Learning pipeline in a couple of steps. We start with a simple...