Amazon SageMaker Studio for Data Scientists Training in Australia

JupyterLab Extensions in SageMaker Studio

Demonstration: SageMaker user interface demo

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

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

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

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

Amazon SageMaker Model Monitor

Discussion: Case study

Demonstration: Model Monitoring

Accrued cost and shutting down

Updates

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