Mastering Azure Machine Learning

Supercharge and automate your deployments to Azure Machine Learning Batch clusters and Azure Kubernetes Service using Azure Machine Learning workspace

Key Features

• Implement end-to-end machine learning pipelines on Azure
• Train deep learning models in Azure using compute clusters
• Deploy machine learning models using MLOps

Book Description

Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows, including training and deploying models and managing MLOps. This book covers the complete end-to-end machine learning process using Microsoft Azure Machine Learning.

You'll learn how to set up an Azure Machine Learning workspace and ingest and version data, as well as visualize, preprocess, label and enrich the data for machine learning. Next, you'll navigate step-by-step through practical machine learning tasks and implement models for embedding, classification and regression. The book also covers simple and advanced NLP techniques, traditional machine learning models such as boosted trees, modern deep neural network architectures, recommender systems and reinforcement learning, and complex distributed machine learning training techniques - all leveraging Azure Machine Learning. Later, you'll 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 automate entire machine learning life cycle processes using MLOps.

What you will learn

• Understand an end-to-end machine learning pipeline in Azure
• Get to grips with the Azure Machine Learning workspace
• Ingest, analyze, and preprocess datasets for machine learning in Azure
• Train traditional and modern machine learning techniques efficiently using compute clusters
• Deploy machine learning models for batch and real-time scoring
• Understand model interoperability with ONNX, TensorFlow and ML Core

Who This Book Is For

This book is for machine learning engineers, data scientists, and machine learning developers who are familiar with MS Azure ML but now want to create advanced models and make sense of data in the cloud. This book is for those who want to bring powerful analytics into cloud applications. A basic understanding of Python and working knowledge of machine learning are mandatory.

Table of Contents

1. Understanding the End-to-End Machine Learning Process
2. Choosing the Right Machine Learning Service in Azure
3. Preparing the Azure Machine Learning Workspace
4. Integrating Data and Managing Datasets
5. Performing Data Analysis and Visualization
6. Data Preprocessing, Feature Extraction and Labeling
7. Advanced Feature Extraction with NLP
8. Azure Machine Learning Pipelines
9. Building ML Models Using Azure Machine Learning
10. Training Deep Neural Networks on Azure
11. Hyperparameter Tuning and Automated Machine Learning
12. Distributed Machine Learning on Azure
13. Building a Recommendation Engine in Azure
14. Model Deployment, Endpoints, and Operations
15. Model Integration, Interoperability, and Hardware Optimization
16. Bringing Models into Production with MLOps
17. Preparing for a Successful ML Journey