Amazon SageMaker Studio for Data Scientists

Course subjects

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: Analyse and prepare data using Amazon SageMaker Data Wrangler

• Using Amazon EMR

• Hands-On Lab: Analyse 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

Module 3: Model Development (continued)

• SageMaker Debugger

• Hands-On Lab: Analysing, 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 optimisation

• Hands-On Lab: Inferencing with SageMaker Studio

Module 5: Monitoring

• Amazon SageMaker Model Monitor

• Discussion: Case study

• Demonstration: Model Monitoring

Module 6: Managing SageMaker Studio Resources and Updates

• Accrued cost and shutting down

• Updates

Capstone

• Environment setup

• Challenge 1: Analyse 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 optimisation

• 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 Pipelines

Please note: This is an emerging technology course. Course outline is subject to change as needed.