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

Chapter 13: Building a Recommendation Engine in Azure

In the previous chapter, we discussed distributed training methods for ML models, and you learned how to train distributed ML models efficiently in Azure. In this chapter, we will dive into traditional and modern recommendation engines, which often combine technologies and techniques covered in the previous chapters.

First, we will take a quick look at the different types of recommendation engines, what data is needed for each type, and what can be recommended using these different approaches. This will help you understand when to choose from non-personalized, content-based, or rating-based recommenders.

After this, we will dive into content-based recommendations, namely item-item and user-user recommenders, based on feature vectors and similarity. You will learn about cosine distance to measure the similarity between feature vectors and feature engineering techniques to avoid common pitfalls while building content-based recommendation...