Mastering Azure Machine Learning - Second Edition: Execute large-scale end-to-end machine learning with Azure (Paperback)

Körner, Christoph, Alsdorf, Marcel

  • 出版商: Packt Publishing
  • 出版日期: 2022-05-10
  • 售價: $1,610
  • 貴賓價: 9.5$1,530
  • 語言: 英文
  • 頁數: 624
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1803232412
  • ISBN-13: 9781803232416
  • 相關分類: Microsoft AzureMachine Learning
  • 下單後立即進貨 (約3~4週)

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商品描述

Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services

Key Features

- Implement end-to-end machine learning pipelines on Azure
- Train deep learning models using Azure compute infrastructure
- Deploy machine learning models using MLOps

Book Description

Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps.

The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning.

The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets.

By the end of this book, you'll be able to combine all the steps you've learned by building an MLOps pipeline.

What you will learn

- Understand the end-to-end ML pipeline
- Get to grips with the Azure Machine Learning workspace
- Ingest, analyze, and preprocess datasets for ML using the Azure cloud
- Train traditional and modern ML techniques efficiently using Azure ML
- Deploy ML models for batch and real-time scoring
- Understand model interoperability with ONNX
- Deploy ML models to FPGAs and Azure IoT Edge
- Build an automated MLOps pipeline using Azure DevOps

Who this book is for

This book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended.

商品描述(中文翻譯)

加速並自動化您對 Azure Machine Learning 叢集和 Azure Kubernetes 服務的部署,使用 Azure Machine Learning 服務。

主要功能:

- 在 Azure 上實施端到端的機器學習流程
- 使用 Azure 計算基礎架構訓練深度學習模型
- 使用 MLOps 部署機器學習模型

書籍描述:

Azure Machine Learning 是一個雲端服務,用於加速和管理機器學習 (ML) 專案的生命週期,ML 專業人員、資料科學家和工程師可以在日常工作流程中使用。本書涵蓋了使用 Microsoft Azure Machine Learning 的端到端 ML 流程,包括數據準備、執行和記錄 ML 訓練運行、設計訓練和部署流程以及通過 MLOps 管理這些流程。

第一部分向您展示如何設置 Azure Machine Learning 工作區;將數據集載入並進行版本控制;以及為訓練準備、標記和豐富這些數據集。在接下來的兩個部分中,您將了解如何為嵌入、分類和回歸訓練 ML 模型。您將探索高級 NLP 技術、提升樹等傳統 ML 模型、現代深度神經網絡、推薦系統、強化學習以及複雜的分佈式 ML 訓練技術 - 這些都是使用 Azure Machine Learning 實現的。

最後一部分將教您如何使用 Docker、Azure Machine Learning 叢集、Azure Kubernetes 服務和其他部署目標,將訓練好的模型部署為批處理流程或實時評分服務。

通過閱讀本書,您將能夠結合所學的所有步驟,構建一個 MLOps 流程。

您將學到什麼:

- 瞭解端到端的 ML 流程
- 熟悉 Azure Machine Learning 工作區
- 使用 Azure 雲端進行數據載入、分析和預處理
- 使用 Azure ML 高效地訓練傳統和現代 ML 技術
- 為批處理和實時評分部署 ML 模型
- 瞭解 ONNX 的模型互操作性
- 將 ML 模型部署到 FPGAs 和 Azure IoT Edge
- 使用 Azure DevOps 構建自動化的 MLOps 流程

本書適合機器學習工程師、資料科學家和機器學習開發人員,他們希望使用 Microsoft Azure 雲端管理數據集和機器學習實驗,並使用 MLOps 構建企業級 ML 架構。本書還將幫助任何對機器學習感興趣的人探索 ML 流程的重要步驟,並使用 Azure Machine Learning 支持他們,以及構建強大的 ML 雲端應用程序。建議具備基本的 Python 理解和機器學習知識。

目錄大綱

1. Understanding the End-to-End Machine Learning Process
2. Choosing the Right Machine Learning Service in Azure
3. Preparing the Azure Machine Learning Workspace
4. Ingesting Data and Managing Datasets
5. Performing Data Analysis and Visualization
6. Feature Engineering and Labeling
7. Advanced Feature Extraction with NLP
8. Azure Machine Learning Pipelines
9. Building ML Models Using Azure Machine Learning
10. Training Deep Neural Networks on Azure
11. Hyperparameter Tuning and Automated Machine Learning
12. Distributed Machine Learning on Azure
13. Building a Recommendation Engine in Azure
14. Model Deployment, Endpoints, and Operations
15. Model Interoperability, Hardware Optimization, and Integrations
16. Bringing Models into Production with MLOps
17. Preparing for a Successful ML Journey

目錄大綱(中文翻譯)

1. 瞭解端到端機器學習流程
2. 選擇適合的 Azure 機器學習服務
3. 準備 Azure 機器學習工作區
4. 輸入資料和管理資料集
5. 進行資料分析和視覺化
6. 特徵工程和標記
7. 使用 NLP 進行高級特徵提取
8. Azure 機器學習流程
9. 使用 Azure 機器學習建立機器學習模型
10. 在 Azure 上訓練深度神經網路
11. 超參數調整和自動化機器學習
12. 在 Azure 上進行分散式機器學習
13. 在 Azure 上建立推薦引擎
14. 模型部署、端點和操作
15. 模型互通性、硬體優化和整合
16. 使用 MLOps 將模型投入生產
17. 為成功的機器學習之旅做好準備