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Mastering Azure Machine Learning

Mastering Azure Machine Learning

By : Körner, Alsdorf
4.5 (15)
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Mastering Azure Machine Learning

Mastering Azure Machine Learning

4.5 (15)
By: Körner, Alsdorf
Buy this Book

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)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
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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
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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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Chapter 1: Understanding the End-to-End Machine Learning Process

Welcome to the second edition of Mastering Azure Machine Learning. In this first chapter, we want to give you an understanding of what kinds of problems require the use of machine learning (ML), how the full ML process unfolds, and what knowledge is required to navigate this vast terrain. You can view it as an introduction to ML and an overview of the book itself, where for most topics we will provide you with a reference to upcoming chapters so that you can easily find your way around the book.

In the first section, we will ask ourselves what ML is, when we should use it, and where it comes from. In addition, we will reflect on how ML is just another form of programming.

In the second section, we will lay the mathematical groundwork you require to process data, and we will understand that the data you work with probably cannot be fully trusted. Further, we will look at different classes of ML algorithms, how they are defined, and how we can define the performance of a trained model.

Finally, in the third section, we will have a look at the end-to-end process of an ML project. We will understand where to get data from, how to preprocess data, how to choose a fitting model, and how to deploy this model into production environments. This will also get us into the topic of ML operations, known as MLOps.

In this chapter, we will cover the following topics:

  • Grasping the idea behind ML
  • Understanding the mathematical basis for statistical analysis and ML modeling
  • Discovering the end-to-end ML process
End of Section 1
Next Section
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