Azure Machine Learning Engineering: Deploy, fine-tune, and optimize ML models using Microsoft Azure

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

Key Features

• Automate complete machine learning solutions using Microsoft Azure
• Understand how to productionize machine learning models
• Get to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learning

Book Description

Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.

Throughout the book, you'll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You'll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework.

By the end of this Azure Machine Learning book, you'll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.

What you will learn

• Train ML models in the Azure Machine Learning service
• Build end-to-end ML pipelines
• Host ML models on real-time scoring endpoints
• Mitigate bias in ML models
• Get the hang of using an MLOps framework to productionize models
• Simplify ML model explainability using the Azure Machine Learning service and Azure Interpret

Who this book is for

Machine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered.

Table of Contents

1. Introducing Azure Machine Learning
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 Azure Machine Learning
11. Using Distributed Training in AMLS

Sina Fakhraee, Ph.D., is currently working at Microsoft as an enterprise data scientist and senior cloud solution architect. He has helped customers to successfully migrate to Azure by providing best practices around data and AI architectural design and by helping them implement AI/ML solutions on Azure. Prior to working at Microsoft, Sina worked at Ford Motor Company as a product owner for Ford’s AI/ML platform. Sina holds a Ph.D. degree in computer science and engineering from Wayne State University and prior to joining the industry, he taught various undergrad and grad computer science courses part time.

Balamurugan Balakreshnan is a principal cloud solution architect at Microsoft Data/AI Architect and Data Science. He has provided leadership on digital transformations with AI and cloud-based digital solutions. He has also provided leadership in terms of ML, the IoT, big data, and advanced analytical solutions.

Megan Masanz is a principal cloud solution architect at Microsoft focused on data, AI, and data science, passionately enabling organizations to address business challenges through the establishment of strategies and road maps for the planning, design, and deployment of Azure Cloud-based solutions. Megan is adept at paving the path to data science via computer science given her master’s in computer science with a focus on data science.