Audience
This course is intended for:
• Experienced data scientists who are proficient in ML and deep learning fundamentals
Prerequisites
Course level: Advanced
We recommend that all attendees of this course have:
• 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 digital or classroom training
Duration
3 Days. Hands On
Course Objectives
Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve productivity at every step of the ML lifecycle.
In this course, you will learn to:
• Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio
Course Content
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 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