Amazon SageMaker Getting Started

Overview

In this course, you will learn the benefits and technical concepts of Amazon SageMaker AI. If you are new to the service, you will learn how to start using Amazon SageMaker AI through a demonstration using the AWS Management Console.

You will use a Jupyter notebook instance to train and generate prediction using a machine learning model.

Syllabus

• Introduction to Amazon SageMaker
Overview of Amazon SageMaker and its services
Key benefits and use cases
Understanding the AI/ML workflow
• Setting Up Your Environment
AWS account setup
Navigating the AWS Management Console
Initial introduction to SageMaker resources
• Understanding SageMaker Components
SageMaker Studio
Jupyter notebooks
SageMaker Model Building Pipelines
SageMaker Ground Truth and Data Wrangler
• Data Preparation
Importing and preparing datasets
Exploring SageMaker data storage options
Introduction to SageMaker Feature Store
• Building and Training Models
Setting up Jupyter notebook instances
Selecting and configuring training algorithms
Understanding hyperparameter tuning
Monitoring training jobs
• Model Evaluation
Evaluating model accuracy and performance
Using metrics and validation strategies
Demonstrating model explainability
• Deploying and Utilizing Models
Deploying models with SageMaker
Setting up endpoints for real-time predictions
Batch transform for asynchronous predictions
Integrating with other AWS services
• Managing and Optimizing Models
Model monitoring and updating
Scaling models for production use
Cost management strategies
• Hands-On Demonstration
Step-by-step guide to creating a Jupyter notebook instance
Training a machine learning model
Generating predictions and evaluating results
• Conclusion and Next Steps
Summary of key learning points
Additional resources for continued learning
Suggestions for practical application and project ideas

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Subjects

Programming