Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
暫譯: Amazon SageMaker 最佳實踐：在 Amazon SageMaker 上構建成功機器學習解決方案的有效技巧與建議

Muppala, Sireesha, Defauw, Randy, Eigenbrode, Shelbee

Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
  • 出版商: Packt Publishing
  • 出版日期: 2021-09-24
  • 售價: $2,000
  • 貴賓價: 9.5 折 $1,900
  • 語言: 英文
  • 頁數: 348
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1801070520
  • ISBN-13: 9781801070522
  • 相關分類: Maker、Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

Key Features

  • Learn best practices for all phases of building machine learning solutions - from data preparation to monitoring models in production
  • Automate end-to-end machine learning workflows with Amazon SageMaker and related AWS
  • Design, architect, and operate machine learning workloads in the AWS Cloud

Book Description

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions.

By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.

What you will learn

  • Perform data bias detection with AWS Data Wrangler and SageMaker Clarify
  • Speed up data processing with SageMaker Feature Store
  • Overcome labeling bias with SageMaker Ground Truth
  • Improve training time with the monitoring and profiling capabilities of SageMaker Debugger
  • Address the challenge of model deployment automation with CI/CD using the SageMaker model registry
  • Explore SageMaker Neo for model optimization
  • Implement data and model quality monitoring with Amazon Model Monitor
  • Improve training time and reduce costs with SageMaker data and model parallelism

Who this book is for

This book is for expert data scientists responsible for building machine learning applications using Amazon SageMaker. Working knowledge of Amazon SageMaker, machine learning, deep learning, and experience using Jupyter Notebooks and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book.

商品描述(中文翻譯)

**主要特點**

- 學習構建機器學習解決方案各階段的最佳實踐 - 從數據準備到監控生產中的模型
- 使用 Amazon SageMaker 和相關的 AWS 自動化端到端的機器學習工作流程
- 在 AWS 雲端設計、架構和運行機器學習工作負載

**書籍描述**

Amazon SageMaker 是一個完全管理的 AWS 服務，提供構建、訓練、部署和監控機器學習模型的能力。本書首先對 Amazon SageMaker 的功能進行高層次的概述，這些功能與機器學習過程的各個階段相對應，以幫助建立正確的基礎。您將學習有效的策略來解決數據科學挑戰，例如大規模處理數據、數據準備、連接大數據管道、識別數據偏見、運行 A/B 測試以及使用 Amazon SageMaker 進行模型可解釋性。隨著進展，您將了解如何應對大規模訓練的挑戰，包括如何使用大型數據集同時節省成本、監控訓練資源以識別瓶頸、加速長時間的訓練作業，以及跟踪為共同目標訓練的多個模型。接下來，您將發現如何將 Amazon SageMaker 與其他 AWS 整合，以構建可靠、成本優化和自動化的機器學習應用程序。此外，您將構建與 MLOps 原則集成的 ML 管道，並應用最佳實踐來構建安全且高效的解決方案。

到本書結束時，您將能夠自信地將 Amazon SageMaker 的廣泛功能應用於全範圍的機器學習工作流程。

**您將學到的內容**

- 使用 AWS Data Wrangler 和 SageMaker Clarify 進行數據偏見檢測
- 使用 SageMaker Feature Store 加速數據處理
- 使用 SageMaker Ground Truth 克服標記偏見
- 利用 SageMaker Debugger 的監控和分析能力改善訓練時間
- 使用 SageMaker 模型註冊表解決模型部署自動化的挑戰，並實現 CI/CD
- 探索 SageMaker Neo 進行模型優化
- 使用 Amazon Model Monitor 實施數據和模型質量監控
- 通過 SageMaker 數據和模型並行性改善訓練時間並降低成本

**本書適合誰**

本書適合負責使用 Amazon SageMaker 構建機器學習應用程序的專家數據科學家。預期具備 Amazon SageMaker、機器學習、深度學習的工作知識，以及使用 Jupyter Notebooks 和 Python 的經驗。對 AWS 相關的數據、安全性和監控的基本知識將幫助您充分利用本書。

目錄大綱

  1. Amazon SageMaker Overview
  2. Data Science Environments
  3. Data Labeling with Amazon SageMaker Ground Truth
  4. Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
  5. Centralized Feature Repository with Amazon SageMaker Feature Store
  6. Training and Tuning at Scale
  7. Profile Training Jobs with Amazon SageMaker Debugger
  8. Managing Models at Scale Using a Model Registry
  9. Updating Production Models Using Amazon SageMaker Endpoint Production Variants
  10. Optimizing Model Hosting and Inference Costs
  11. Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify
  12. Machine Learning Automated Workflows
  13. Well-Architected Machine Learning with Amazon SageMaker
  14. Managing SageMaker Features Across Accounts

目錄大綱(中文翻譯)


  1. Amazon SageMaker Overview

  2. Data Science Environments

  3. Data Labeling with Amazon SageMaker Ground Truth

  4. Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing

  5. Centralized Feature Repository with Amazon SageMaker Feature Store

  6. Training and Tuning at Scale

  7. Profile Training Jobs with Amazon SageMaker Debugger

  8. Managing Models at Scale Using a Model Registry

  9. Updating Production Models Using Amazon SageMaker Endpoint Production Variants

  10. Optimizing Model Hosting and Inference Costs

  11. Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify

  12. Machine Learning Automated Workflows

  13. Well-Architected Machine Learning with Amazon SageMaker

  14. Managing SageMaker Features Across Accounts

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