SageMaker pipeline demonstration - Amazon SageMaker Tutorial

Contents

• Introduction Introduction

  • Machine learning with SageMaker: Introduction

    1m 25s
• 1. Data Ingestion and Storage Basics 1. Data Ingestion and Storage Basics

  • Module introduction

    31s
  • Learning objectives

    31s
  • Course overview

    2m 45s
  • Overview of common data formats

    10m 11s
  • Ingesting data with Amazon S3 and SageMaker Data Wrangler

    10m 48s
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    Data ingestion demonstration

    21m 2s
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    Data storage optimization and transfer in AWS

    13m 40s
• 2. Data Transformation and Feature Engineering with SageMaker 2. Data Transformation and Feature Engineering with SageMaker

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    Learning objectives

    28s
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    Data cleaning and preprocessing with SageMaker Data Wrangler

    10m 27s
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    Data preprocessing demonstration

    15m 21s
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    Feature scaling and encoding techniques

    8m 30s
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    Handling missing values and outliers

    10m 34s
• 3. Preparing Data for Modeling 3. Preparing Data for Modeling

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    Learning objectives

    30s
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    Validating data quality with AWS tools

    8m 8s
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    Configuring data for SageMaker training jobs

    18m 22s
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    Managing SageMaker Feature Store

    8m 28s
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    SageMaker Feature Store demonstration

    18m 41s
• 4. Choosing and Training Models in SageMaker 4. Choosing and Training Models in SageMaker

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    Module introduction

    30s
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    Learning objectives

    32s
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    Overview of SageMaker built-in algorithms and JumpStart models

    8m 13s
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    SageMaker algorithms demonstration

    25m 3s
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    Setting up and running SageMaker training jobs

    6m 14s
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    SageMaker training demonstration

    21m 48s
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    Hyperparameter tuning with SageMaker automatic model tuning

    7m 42s
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    Hyperparameter tuning demonstration

    21m 48s
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    Preventing overfitting and underfitting

    8m 3s
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    Model over/underfitting demonstration

    13m 39s
• 5. Model Evaluation and Bias Detection 5. Model Evaluation and Bias Detection

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    Learning objectives

    39s
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    Model evaluation metrics: Accuracy, precision, and recall

    9m 39s
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    Using SageMaker Clarify for bias detection and interpretability

    7m 40s
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    Comparing model performance using A/B testing

    5m 47s
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    Model A/B testing demonstration

    6m 26s
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    Managing model versions with SageMaker Model Registry

    5m 55s
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    Model registry demonstration

    10m 48s
• 6. Deploying Models with SageMaker 6. Deploying Models with SageMaker

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    Module introduction

    35s
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    Learning objectives

    34s
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    Real-time inference with SageMaker endpoints

    9m 58s
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    Real-time inference demonstration

    8m 51s
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    Batch inference and asynchronous inference

    6m 29s
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    Batch and asynchronous inference demonstration

    14m 50s
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    Using SageMaker Neo for edge deployment

    7m 32s
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    SageMaker edge deployment demonstration

    12m 13s
• 7. Automating ML Workflows with SageMaker Pipelines 7. Automating ML Workflows with SageMaker Pipelines

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    Learning objectives

    31s
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    Building and automating ML pipelines in SageMaker

    6m 40s
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    SageMaker pipeline demonstration

    34m 3s
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    Integrating data processing and training steps

    6m 56s
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    Training and data processing in SageMaker pipelines demonstration

    10m 17s
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    Triggering pipelines with EventBridge for retraining

    7m 15s
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    Triggering SageMaker pipelines via EventBridge demonstration

    9m 51s
• 8. Monitoring and Optimizing ML Solutions 8. Monitoring and Optimizing ML Solutions

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    Module introduction

    34s
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    Learning objectives

    33s
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    Using SageMaker Model Monitor for data drift and quality

    5m 49s
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    SageMaker Model Monitor demonstration

    6m 30s
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    Setting up alerts and CloudWatch dashboards

    7m 41s
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    Cost optimization with auto-scaling and SageMaker Savings Plans

    7m 9s
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    SageMaker auto scaling demonstration

    9m 31s
• 9. Securing ML Models and Data in SageMaker 9. Securing ML Models and Data in SageMaker

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    Learning objectives

    32s
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    IAM roles and permissions for SageMaker

    9m 15s
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    IAM demonstration

    5m 9s
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    VPC configurations for secure endpoint deployment

    8m 12s
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    VPC demonstration

    13m 15s
• Summary Summary

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    Machine learning with SageMaker summary

    39s