Amazon SageMaker Studio for Data Scientists Duration: 3 Days

Course details

Day 1

Module 1: Amazon SageMaker Studio Setup
• JupyterLab Extensions in SageMaker Studio
• Demonstration: SageMaker user interface demo

Module 2: Data Processing
• Using SageMaker Data Wrangler for data processing
• Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
• Using Amazon EMR
• Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
• Using AWS Glue interactive sessions
• Using SageMaker Processing with custom scripts
• Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker
Python SDK
• SageMaker Feature Store
• Hands-On Lab: Feature engineering using SageMaker Feature Store

Module 3: Model Development
• SageMaker training jobs
• Built-in algorithms
• Bring your own script
• Bring your own container
• SageMaker Experiments
• Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning
Models

Day 2

Module 3: Model Development (continued)
• SageMaker Debugger
• Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
• Automatic model tuning
• SageMaker Autopilot: Automated ML
• Demonstration: SageMaker Autopilot
• Bias detection
• Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
• SageMaker Jumpstart

Module 4: Deployment and Inference
• SageMaker Model Registry
• SageMaker Pipelines
• Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker
Studio
• SageMaker model inference options
• Scaling
• Testing strategies, performance, and optimization
• Hands-On Lab: Inferencing with SageMaker Studio

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