• 1. 快速入门
    • 1. 创建Notebook
    • 2. 使用AWS Marketplace上的模型
    • 3. SageMaker Studio
    • 4. SageMaker Canvas
    • 5. SageMaker Studio架构
    • 6. 特征工程
    • 7. 训练和部署
    • 8. SageMaker 推理费用
    • 9. SageMaker Quota
    • 10. 删除Domain CLI
  • 2. Data Wrangler - I
    • 1. 数据准备
    • 2. Data Wrangler架构
    • 3. 创建Data Flow
    • 4. Exploratory Data Analysis
    • 5. 直方图和散点图
    • 6. Bias Detection - 偏差检测
    • 7. 自定义可视化
    • 8. 特征相关性
    • 9. Multicollinearity
  • 3. Data Wrangler - II
    • 1. 检测重复行
    • 2. Quick Model
    • 3. 特征工程 - 自定义转换
    • 4. 特征工程 - 删除冗余列
    • 5. 特征工程 - 处理缺失值和异常值
    • 6. 特征工程 - 处理分类特征
    • 7. 特征工程 - Normalize Numeric Features
    • 8. 特征工程 - Balance Classes
    • 9. Quick Model
    • 10. 导出 Data Wrangler Flow
    • 11. Programatic Export
  • 5. RTB Workshop
    • 1. 环境搭建
    • 2. 生成基本配置
    • 3. 下载原始数据
    • 4. SageMaker Studio连接到 EMR 集群
    • 5. 数据处理和特征工程
    • 6. 探索数据
    • 7. 模型训练
    • 8. 推理模块
    • 9. 附: 在SparkMagic上安装库
  • 6. Feature Store
    • 1. 创
    • 2. AWS
    • 3. AWS
    • 4. 4
    • 5. 55
    • 6. AWS
    • 7. 77
    • 103. Self-Paced Lab
    • 104. Module 1: Introduction to SageMaker Feature Store
    • 105. Prepare datasets
    • 106. Create feature group and ingest data
    • 107. Module 2: Working with offline store
    • 108. Working with offline store
    • 109. Search and Discovery using Feature-Level Metadata
    • 110. Speed ML Development with Apache Iceberg offline store compaction
    • 111. Module 3: Feature transformation and training
    • 112. Batch Scoring using a pre-trained XGBoost model
    • 113. Batch Scoring using a pre-trained XGBoost model (Parquet)
    • 114. Update Feature Group (Optional notebook)
    • 115. Feature transformation and training
    • 116. Module 4: Working with online store
    • 117. Working with online store
    • 118. Inference Patterns - endpoint based feature lookup
    • 119. Inference Patterns - Inference pipeline based feature lookup
    • 120. Module 5: Scalable Batch Ingestion
    • 121. Partition datasets
    • 122. Scikitlearn processor
    • 123. Apache Spark processor
    • 124. Feature Ingestion using Data Wrangler
    • 125. Module 6: Automated pipeline
    • 126. Module 7: Feature monitoring
    • 127. Module 8: ML Lineage Tracking
    • 128. Module 9: Security
    • 129. Granular access control in AWS Lake Formation (for Offline Feature Store)
    • 130. Access control to online and offline feature store using IAM policies
    • 131. Cleanup

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