Practical Data Science with Amazon SageMaker (GK0630)

Module 1: Introduction to Machine Learning

• • Benefits of machine learning (ML)
• • Types of ML approaches
• • Framing the business problem
• • Prediction quality
• • Processes, roles, and responsibilities for ML projects

Module 2: Preparing a Dataset

• • Data analysis and preparation
• • Data preparation tools
• • Demonstration: Review Amazon SageMaker Studio and Notebooks
• • Hands-On Lab: Data Preparation with SageMaker Data Wrangler

Module 3: Training a Model

• • Steps to train a model
• • Choose an algorithm
• • Train the model in Amazon SageMaker
• • Hands-On Lab: Training a Model with Amazon SageMaker
• • Amazon CodeWhisperer
• • Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks

Module 4: Evaluating and Tuning a Model

• • Model evaluation
• • Model tuning and hyperparameter optimization
• • Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker

Module 5: Deploying a Model

• • Model deployment
• • Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction

Module 6: Operational Challenges

• • Responsible ML
• • ML team and MLOps
• • Automation
• • Monitoring
• • Updating models (model testing and deployment)

Module 7: Other Model-Building Tools

• • Different tools for different skills and business needs
• • No-code ML with Amazon SageMaker Canvas
• • Demonstration: Overview of Amazon SageMaker Canvas
• • Amazon SageMaker Studio Lab
• • Demonstration: Overview of SageMaker Studio Lab
• • (Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint