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

Sinopsis

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

"Sinopsis" puede pertenecer a otra edición de este libro.