Amazon SageMaker Studio for Data Scientists (GK110001)

Day 1

• • Module 1: Amazon SageMaker Studio Setup
• • JupyterLab Extensions in SageMaker Studio
• • Demonstration: SageMaker user interface demo
• • Module 2: Data Processing
• • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
• • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
• • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
• • Hands-On Lab: Feature engineering using SageMaker Feature Store
• • Using SageMaker Data Wrangler for data processing
• • Using Amazon EMR
• • Using AWS Glue interactive sessions
• • Using SageMaker Processing with custom scripts
• • SageMaker Feature Store
• • Module 3: Model Development
• • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
• • SageMaker training jobs
• • Built-in algorithms
• • Bring your own script
• • Bring your own container
• • SageMaker Experiments

Day 2

• • Module 3: Model Development (continued)
• • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
• • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
• • SageMaker Debugger
• • Automatic model tuning
• • SageMaker Autopilot: Automated ML
• • Demonstration: SageMaker Autopilot
• • Bias detection
• • SageMaker Jumpstart
• • Module 4: Deployment and Inference
• • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
• • Hands-On Lab: Inferencing with SageMaker Studio
• • SageMaker Model Registry
• • SageMaker Pipelines
• • SageMaker model inference options
• • Scaling
• • Testing strategies, performance, and optimization
• • Module 5: Monitoring
• • Amazon SageMaker Model Monitor
• • Discussion: Case study
• • Demonstration: Model Monitoring

Day 3

• • Module 6: Managing SageMaker Studio Resources and Updates
• • Accrued cost and shutting down
• • Updates
• • Capstone
• • Environment setup
• • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
• • Challenge 2: Create feature groups in SageMaker Feature Store
• • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
• • (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization
• • Challenge 5: Evaluate the model for bias using SageMaker Clarify
• • Challenge 6: Perform batch predictions using model endpoint
• • (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline