Azure Machine Learning Engineering: Deploy, fine-tune, and optimize ML models using Microsoft Azure
暫譯: Azure 機器學習工程:使用 Microsoft Azure 部署、微調和優化 ML 模型
Fakhraee, Sina, Balakreshnan, Balamurugan, Masanz, Megan
- 出版商: Packt Publishing
- 出版日期: 2023-01-20
- 售價: $1,450
- 貴賓價: 9.5 折 $1,378
- 語言: 英文
- 頁數: 362
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1803239301
- ISBN-13: 9781803239309
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相關分類:
Microsoft Azure、Machine Learning
立即出貨 (庫存=1)
相關主題
商品描述
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.
商品描述(中文翻譯)
使用 Azure Machine Learning Service 完整構建並生產化端到端的機器學習解決方案
主要特點
- 使用 Microsoft Azure 自動化完整的機器學習解決方案
- 了解如何將機器學習模型生產化
- 熟悉監控、MLOps、深度學習、分散式訓練和強化學習
書籍描述
在將機器學習 (ML) 工作負載生產化的過程中,數據科學家面臨著由於部署和運行 ML 模型所涉及的無數因素而產生的各種挑戰。本書提供了常見問題的解決方案、關鍵概念的詳細解釋,以及使用 Azure Machine Learning 服務生產化 ML 工作負載的逐步指導。您將看到與 Microsoft Azure 合作的數據科學家和 ML 工程師如何利用這本實用指南,將他們的知識應用於大規模訓練和部署 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。您將熟悉實時和批量評分模型、解釋模型以獲得業務信任、減輕模型偏見,以及使用 MLOps 框架開發解決方案。
在閱讀完這本 Azure Machine Learning 書籍後,您將能夠使用 Azure Machine Learning 服務為實時場景構建和部署端到端的 ML 解決方案。
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 管道
- 在實時評分端點上託管 ML 模型
- 減輕 ML 模型中的偏見
- 熟悉使用 MLOps 框架來生產化模型
- 使用 Azure Machine Learning 服務和 Azure Interpret 簡化 ML 模型的可解釋性
本書適合誰
希望轉向 ML 工程角色的機器學習工程師和數據科學家將會發現這本 AMLS 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。
目錄大綱
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
目錄大綱(中文翻譯)
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