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)
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

Automatic optimization through reinforcement learning

You can improve your recommendations by providing online training techniques, which will retrain your recommender systems after every user-item interaction. By replacing the feedback function with a reward function and adding a reinforcement learning model, we can now make recommendations, make decisions, and optimize choices that optimize the reward function.

This is a fantastic new approach to training recommender models. The Azure Personalizer service offers exactly this functionality – to make and optimize decisions and choices by providing contextual features and a reward function to the user. Azure Personalizer uses contextual bandits, an approach to reinforcement learning that is framed around making decisions or choices between discrete actions in a given context.

Important Note

Under the hood, Azure Personalizer uses the Vowpal Wabbit (https://github.com/VowpalWabbit/vowpal_wabbit/wiki) learning system from...