Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
暫譯: 開始使用 Amazon SageMaker Studio:學習在 SageMaker 機器學習 IDE 中構建端到端的機器學習專案

Hsieh, Michael

  • 出版商: Packt Publishing
  • 出版日期: 2022-03-31
  • 售價: $1,840
  • 貴賓價: 9.5$1,748
  • 語言: 英文
  • 頁數: 326
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1801070156
  • ISBN-13: 9781801070157
  • 相關分類: MakerMachine Learning
  • 海外代購書籍(需單獨結帳)

商品描述

Build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code

Key Features

- Understand the ML lifecycle in the cloud and its development on Amazon SageMaker Studio
- Learn to apply SageMaker features in SageMaker Studio for ML use cases
- Scale and operationalize the ML lifecycle effectively using SageMaker Studio

Book Description

Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment.

In this book, you'll start by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio.

By the end of this book, you'll have learned ML best practices regarding Amazon SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases.

What you will learn

- Explore the ML development life cycle in the cloud
- Understand SageMaker Studio features and the user interface
- Build a dataset with clicks and host a feature store for ML
- Train ML models with ease and scale
- Create ML models and solutions with little code
- Host ML models in the cloud with optimal cloud resources
- Ensure optimal model performance with model monitoring
- Apply governance and operational excellence to ML projects

Who this book is for

This book is for data scientists and machine learning engineers who are looking to become well-versed with Amazon SageMaker Studio and gain hands-on machine learning experience to handle every step in the ML lifecycle, including building data as well as training and hosting models. Although basic knowledge of machine learning and data science is necessary, no previous knowledge of SageMaker Studio and cloud experience is required.

商品描述(中文翻譯)

**使用 Amazon SageMaker Studio 建立生產級機器學習模型,這是雲端中的第一個整合開發環境,並使用真實的機器學習範例和程式碼**

#### 主要特點

- 了解雲端中的機器學習 (ML) 生命週期及其在 Amazon SageMaker Studio 上的開發
- 學習如何在 SageMaker Studio 中應用 SageMaker 功能以滿足機器學習的使用案例
- 有效地使用 SageMaker Studio 擴展和運營機器學習生命週期

#### 書籍描述

Amazon SageMaker Studio 是第一個針對機器學習 (ML) 的整合開發環境 (IDE),旨在整合 ML 工作流程:數據準備、特徵工程、統計偏差檢測、自動化機器學習 (AutoML)、訓練、托管、機器學習可解釋性、監控和 MLOps,所有這些都在一個環境中進行。

在本書中,您將首先探索 Amazon SageMaker Studio 中可用的功能,以分析數據、開發機器學習模型,並將模型生產化以達成您的目標。隨著進展,您將學習這些功能如何協同工作,以解決在生產中建立機器學習模型時常見的挑戰。之後,您將了解如何有效地使用 SageMaker Studio 擴展和運營機器學習生命週期。

在本書結束時,您將學習到有關 Amazon SageMaker Studio 的機器學習最佳實踐,並能夠提高機器學習開發生命週期的生產力,輕鬆建立和部署模型以滿足您的機器學習使用案例。

#### 您將學到什麼

- 探索雲端中的機器學習開發生命週期
- 了解 SageMaker Studio 的功能和用戶界面
- 透過點擊建立數據集並為機器學習托管特徵庫
- 輕鬆且可擴展地訓練機器學習模型
- 使用少量程式碼創建機器學習模型和解決方案
- 在雲端中使用最佳雲資源托管機器學習模型
- 透過模型監控確保最佳模型性能
- 對機器學習專案應用治理和運營卓越

#### 本書適合誰

本書適合希望熟悉 Amazon SageMaker Studio 的數據科學家和機器學習工程師,並獲得實際的機器學習經驗,以處理機器學習生命週期中的每個步驟,包括數據構建、模型訓練和托管。雖然需要具備基本的機器學習和數據科學知識,但不需要先前的 SageMaker Studio 和雲端經驗。

目錄大綱

1. Machine Learning and Its Life Cycle in the Cloud
2. Introducing Amazon SageMaker Studio
3. Data Preparation with SageMaker Data Wrangler
4. Building a Feature Repository with SageMaker Feature Store
5. Building and Training ML Models with SageMaker Studio IDE
6. Detecting ML Bias and Explaining Models with SageMaker Clarify
7. Hosting ML Models in the Cloud: Best Practices
8. Jumpstarting ML with SageMaker JumpStart and Autopilot
9. Training ML Models at Scale in SageMaker Studio
10. Monitoring ML Models in Production with SageMaker Model Monitor
11. Operationalize ML Projects with SageMaker Projects, Pipelines and Model Registry

目錄大綱(中文翻譯)

1. Machine Learning and Its Life Cycle in the Cloud

2. Introducing Amazon SageMaker Studio

3. Data Preparation with SageMaker Data Wrangler

4. Building a Feature Repository with SageMaker Feature Store

5. Building and Training ML Models with SageMaker Studio IDE

6. Detecting ML Bias and Explaining Models with SageMaker Clarify

7. Hosting ML Models in the Cloud: Best Practices

8. Jumpstarting ML with SageMaker JumpStart and Autopilot

9. Training ML Models at Scale in SageMaker Studio

10. Monitoring ML Models in Production with SageMaker Model Monitor

11. Operationalize ML Projects with SageMaker Projects, Pipelines and Model Registry