Amazon SageMaker Studio for Data Scientists

• Experience using ML frameworks
• Python programming experience
• At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
• AWS Technical Essentials

Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio

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
• Using AWS Glue interactive sessions
• Using SageMaker Processing with custom scripts

Module 3: Model Development

• SageMaker training jobs
• Built-in algorithms
• Bring your own script
• Bring your own container
• SageMaker Experiments

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
• SageMaker Jumpstart

Module 4: Deployment and Inference

• 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

• 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
• Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
• Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
• Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
• Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
• Hands-On Lab: Inferencing with SageMaker Studio