Mastering Azure Machine Learning - Second Edition: Execute large-scale end-to-end machine learning with Azure (Paperback)
暫譯: 精通 Azure 機器學習（第二版）：使用 Azure 執行大規模端到端機器學習（平裝本）

Körner, Christoph, Alsdorf, Marcel

Mastering Azure Machine Learning - Second Edition: Execute large-scale end-to-end machine learning with Azure (Paperback)
  • 出版商: Packt Publishing
  • 出版日期: 2022-05-10
  • 售價: $1,760
  • 貴賓價: 9.5 折 $1,672
  • 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 Azure、Machine Learning

    • 海外代購書籍(需單獨結帳)

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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)

超充電並自動化您的部署至 Azure Machine Learning 叢集和 Azure Kubernetes Service，使用 Azure Machine Learning 服務

主要特點

- 在 Azure 上實現端到端的機器學習管道
- 使用 Azure 計算基礎設施訓練深度學習模型
- 使用 MLOps 部署機器學習模型

書籍描述

Azure Machine Learning 是一項雲端服務，用於加速和管理機器學習 (ML) 專案生命週期，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、設計訓練和部署管道，以及通過 MLOps 管理這些管道。

第一部分將向您展示如何設置 Azure Machine Learning 工作區；導入和版本控制數據集；以及預處理、標記和豐富這些數據集以進行訓練。在接下來的兩個部分中，您將發現如何豐富和訓練用於嵌入、分類和回歸的 ML 模型。您將探索先進的自然語言處理 (NLP) 技術、傳統的機器學習模型如增強樹、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 - 所有這些都使用 Azure Machine Learning。

最後一部分將教您如何使用 Docker、Azure Machine Learning 叢集、Azure Kubernetes Services 和其他部署目標將訓練好的模型部署為批次管道或實時評分服務。

在本書結束時，您將能夠通過構建 MLOps 管道來結合您所學的所有步驟。

您將學到的內容

- 理解端到端的 ML 管道
- 熟悉 Azure Machine Learning 工作區
- 使用 Azure 雲端導入、分析和預處理 ML 的數據集
- 使用 Azure ML 高效訓練傳統和現代的機器學習技術
- 部署 ML 模型以進行批次和實時評分
- 理解與 ONNX 的模型互操作性
- 將 ML 模型部署到 FPGA 和 Azure IoT Edge
- 使用 Azure DevOps 構建自動化的 MLOps 管道

本書適合誰

本書適合希望使用 Microsoft Azure 雲端管理其數據集和機器學習實驗的機器學習工程師、資料科學家和機器學習開發人員，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 來支持他們，並構建強大的 ML 雲端應用程式。建議具備基本的 Python 知識和機器學習的相關知識。

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. 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

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