Amazon SageMaker Studio for Data Scientists

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

• 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

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

Labs - Please note: The labs for your AWS course will be delivered through AWS Builder labs. In order to access these labs you will need to have an Amazon BuilderID. You can set up your new Amazon account here. Please ensure that you have set up this Amazon BuilderID in advance of attending your class.

Courseware – Please note: In order to access your digital course materials you are required to set up a Gilmore account in advance of attending your course. To do this please follow this link.

Please also be aware that in order to access your materials and Labs it is important that your device and network should not restrict access to AWS or Vitalsource content. For that reason, AWS recommend NOT using a Corporate laptop with any security restrictions in place or the use of a VPN.