Overview
Dive into machine learning with Amazon SageMaker AI, creating Jupyter notebooks to train models and generate predictions through hands-on demonstrations using the AWS Management Console.
Syllabus
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- 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
Taught by
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On-Demand
