finelybook
Azure Machine Learning Engineering: Deploy, fine-tune, and optimize ML models using Microsoft Azure
by Sina Fakhraee(Author), Balamurugan Balakreshnan(Author), Megan Masanz(Author)
Publisher Finelybook 出版社: Packt Publishing (January 20, 2023)
Language 语言: English
pages 页数: 362 pages
ISBN-10 书号: 1803239301
ISBN-13 书号: 9781803239309
Book Description
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
Introducing Azure Machine Learning
Working with Data in AMLS
Training Machine Learning Models in AMLS
Tuning Your Models with AMLS
Azure Automated Machine Learning
Deploying ML Models for Real-Time Inferencing
Deploying ML Models for Batch Scoring
Responsible AI
Productionizing Your Workload with MLOps
Using Deep Learning in Azure Machine Learning
Using Distributed Training in AMLS
