Azure Machine Learning Engineering

Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service.

Key Features

• Automate full end-to-end machine learning solutions using Microsoft Azure
• Understand how to productionalize Machine Learning Models.
• Learn Monitoring, MLOps, Deep Learning, Distributed Training and Reinforcement Learning

Book Description

Azure Machine Learning Service to train machine learning models and productionize workloads Data Scientists often have trouble productionizing workloads and this will teach them how to do it. Data Scientists working with Azure will be able to put their knowledge to work with this practical guide to Machine Learning Engineering. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin by training a model in Azure Machine Learning Service followed by step-by-step instructions in productionizing your model. You'll learn how to train, register, and productionize machine learning models using Azure Machine Learning Service. You'll learn how to score models in real-time and in batch, how to explain models to earn business trust, how to mitigate model bias and develop solutions using an MLOps framework.

By the end of this book, you will be able to fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service.

What you will learn

• Train Machine Learning Models in Azure Machine Learning Service
• Build end-to-end Machine Learning Pipelines
• Host Machine Learning Models on Real-Time Scoring Endpoints
• Explain Machine Learning Models and Mitigate Bias
• Use and MLOps Framework to Productionize Models

Who This Book Is For

Machine Learning Engineers and Data Scientists who want to move to ML Engineering roles will find this book useful. Familiarity with Azure Ecosystem is a plus.

Table of Contents

1. Introducing Azure Machine Learning Service
2. Working with Data in AMLS
3. Training Machine Learning Models in AMLS
4. Tuning your models with AMLS
5. Azure Automated Machine Learning
6. Deploying ML Models for Real-Time Inferencing
7. Deploying ML Models for Batch Scoring
8. Responsible AI
9. Productionizing your Workload with MLOps
10. Using Deep Learning in AMLS
11. Using Distributed Training in AMLS