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.

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Table of Contents (23 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
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Conventions used
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1
Section 1: Introduction to Azure Machine Learning
Section 1: Introduction to Azure Machine Learning
Free Chapter
2
Chapter 1: Understanding the End-to-End Machine Learning Process
Chapter 1: Understanding the End-to-End Machine Learning Process
Grasping the idea behind ML
Understanding the mathematical basis for statistical analysis and ML modeling
Discovering the end-to-end ML process
Summary
3
Chapter 2: Choosing the Right Machine Learning Service in Azure
Chapter 2: Choosing the Right Machine Learning Service in Azure
Choosing an Azure service for ML
Managed ML services
Custom ML services
Custom compute services for ML
Summary
4
Chapter 3: Preparing the Azure Machine Learning Workspace
Chapter 3: Preparing the Azure Machine Learning Workspace
Technical requirements
Deploying an Azure Machine Learning workspace
Exploring the Azure Machine Learning service
Running ML experiments with Azure Machine Learning
Summary
5
Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
6
Chapter 4: Ingesting Data and Managing Datasets
Chapter 4: Ingesting Data and Managing Datasets
Technical requirements
Choosing data storage solutions for Azure Machine Learning
Creating a datastore and ingesting data
Using datasets in Azure Machine Learning
Summary
7
Chapter 5: Performing Data Analysis and Visualization
Chapter 5: Performing Data Analysis and Visualization
Technical requirements
Understanding data exploration techniques
Performing data analysis on a tabular dataset
Understanding dimensional reduction techniques
Summary
8
Chapter 6: Feature Engineering and Labeling
Chapter 6: Feature Engineering and Labeling
Technical requirements
Understanding and applying feature engineering
Handling data labeling
Summary
9
Chapter 7: Advanced Feature Extraction with NLP
Chapter 7: Advanced Feature Extraction with NLP
Technical requirements
Understanding categorical data
Building a simple bag-of-words model
Leveraging term importance and semantics
Implementing end-to-end language models
Summary
10
Chapter 8: Azure Machine Learning Pipelines
Chapter 8: Azure Machine Learning Pipelines
Technical requirements
Using pipelines in ML workflows
Building and publishing an ML pipeline
Integrating pipelines with other Azure services
Summary
11
Section 3: The Training and Optimization of Machine Learning Models
Section 3: The Training and Optimization of Machine Learning Models
12
Chapter 9: Building ML Models Using Azure Machine Learning
Chapter 9: Building ML Models Using Azure Machine Learning
Technical requirements
Working with tree-based ensemble classifiers
Training an ensemble classifier model using LightGBM
Summary
13
Chapter 10: Training Deep Neural Networks on Azure
Chapter 10: Training Deep Neural Networks on Azure
Technical requirements
Introduction to Deep Learning
Training a CNN for image classification
Summary
14
Chapter 11: Hyperparameter Tuning and Automated Machine Learning
Chapter 11: Hyperparameter Tuning and Automated Machine Learning
Technical requirements
Finding the optimal model parameters with HyperDrive
Finding the optimal model with Automated Machine Learning
Summary
15
Chapter 12: Distributed Machine Learning on Azure
Chapter 12: Distributed Machine Learning on Azure
Technical requirements
Exploring methods for distributed ML
Using distributed ML in Azure
Summary
16
Chapter 13: Building a Recommendation Engine in Azure
Chapter 13: Building a Recommendation Engine in Azure
Technical requirements
Introduction to recommendation engines
A content-based recommender system
Collaborative filtering – a rating-based recommender system
Combining content and ratings in hybrid recommendation engines
Automatic optimization through reinforcement learning
Summary
17
Section 4: Machine Learning Model Deployment and Operations
Section 4: Machine Learning Model Deployment and Operations
18
Chapter 14: Model Deployment, Endpoints, and Operations
Chapter 14: Model Deployment, Endpoints, and Operations
Technical requirements
Preparations for model deployments
Deploying ML models in Azure
ML operations in Azure
Summary
19
Chapter 15: Model Interoperability, Hardware Optimization, and Integrations
Chapter 15: Model Interoperability, Hardware Optimization, and Integrations
Technical requirements
Model interoperability with ONNX
Hardware optimization with FPGAs
Integrating ML models and endpoints with Azure services
Summary
20
Chapter 16: Bringing Models into Production with MLOps
Chapter 16: Bringing Models into Production with MLOps
Technical requirements
Ensuring reproducible builds and deployments
Validating the code, data, and models
Building an end-to-end MLOps pipeline
Summary
21
Chapter 17: Preparing for a Successful ML Journey
Chapter 17: Preparing for a Successful ML Journey
Remembering the importance of data
Starting with a thoughtful infrastructure
Automating recurrent tasks
Expecting constant change
Thinking about your responsibility
Summary
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22
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Preface

During the last decade, machine learning (ML) has grown from a niche concept worked on in scientific circles to an enterprise-grade toolset that can be used to improve business processes and build intelligent products and services. The main reason is the constant increase in the volume of data being generated globally, requiring distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights. This book will help you improve your knowledge of ML concepts, find the right models for your use cases, and will give you the skillset to run machine learning models and build end-to-end ML pipelines in the Azure cloud.

The book starts with an overview of every step in an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. From there on out, it focuses on the Azure Machine Learning service and takes you through the important processes of data preparation and feature engineering. Following that, the book focuses on ML modeling techniques for different requirements, including advanced feature extraction techniques using natural language processing (NLP), classical ML techniques such as ensemble learning, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. In addition, the book explores how to train, optimize, and tune models using Azure automated machine learning and HyperDrive, and perform model training on distributed training clusters on Azure. Finally, the book covers the deployment of ML models to different target computes such as Azure Machine Learning clusters, Azure Kubernetes Service, and Field Programmable Gate Arrays (FPGAs), along with the setup of MLOps pipelines with Azure DevOps.

By the end of this book, you'll have the foundation to run a well-thought-out ML project from start to finish and will have mastered the tooling available in Azure to train, deploy, and operate ML models and pipelines.

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