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.

完整構建並將端到端機器學習解決方案生產化，使用Azure Machine Learning服務

主要特點

- 使用Microsoft Azure自動化完整的機器學習解決方案
- 理解如何生產化機器學習模型
- 掌握監控、MLOps、深度學習、分散式訓練和強化學習等技術

書籍描述

在將機器學習（ML）工作負載生產化的過程中，數據科學家面臨著各種挑戰，因為涉及到許多因素，需要將ML模型部署並運行起來。本書提供了解決常見問題的解決方案，詳細解釋了基本概念，並提供了使用Azure Machine Learning服務生產化ML工作負載的逐步指南。通過這本實用指南，您將了解使用Microsoft Azure的數據科學家和ML工程師如何通過將知識應用於實踐來培訓和部署大規模的ML模型。

在整本書中，您將學習如何利用Azure Machine Learning服務的強大功能來訓練、註冊和生產化ML模型。您將掌握實時和批量模型評分、解釋模型以獲得業務信任、減輕模型偏差以及使用MLOps框架開發解決方案的方法。

通過閱讀本書，您將能夠使用Azure Machine Learning服務將端到端的ML解決方案部署到生產系統中，應用於實時場景。

您將學到什麼

- 在Azure Machine Learning服務中訓練ML模型
- 構建端到端的ML流程
- 在實時評分端點上托管ML模型
- 減輕ML模型的偏差
- 掌握使用MLOps框架生產化模型的方法
- 使用Azure Machine Learning服務和Azure Interpret簡化ML模型的可解釋性

本書適合對象

希望轉向ML工程師角色的機器學習工程師和數據科學家將會發現這本Azure Machine Learning服務的書籍非常有用。熟悉Azure生態系統將有助於理解所涵蓋的概念。

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

1. 介紹 Azure Machine Learning
2. 在 AMLS 中處理資料
3. 在 AMLS 中訓練機器學習模型
4. 使用 AMLS 調整模型
5. Azure 自動化機器學習
6. 部署用於實時推論的機器學習模型
7. 部署用於批次評分的機器學習模型
8. 負責任的人工智慧
9. 使用 MLOps 將工作負載投入生產
10. 在 Azure Machine Learning 中使用深度學習
11. 在 AMLS 中使用分散式訓練