Practical Data Science with Amazon SageMaker

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