Learning Objectives
The Objectives of this course are as follows:
- Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
- Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
Target Audience
The course is intended for:
- Experienced data scientists
Prerequisite Experience
- Experience using ML frameworks
- Python programming experience
- At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
Course Outline
Amazon SageMaker Studio Setup
- SageMaker user interface demo
- Using SageMaker Data Wrangler for data processing
- Lab 1: Analyze and prepare data using Amazon SageMaker Data Wrangler
- Using Amazon EMR
- Lab 2: Analyze and prepare data at scale using Amazon EMR
- Using AWS Glue interactive sessions
- Using SageMaker Processing with custom scripts
- Lab 3: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
- SageMaker Feature Store
- Lab 4: Feature engineering using SageMaker Feature Store
- SageMaker training jobs
- Built-in algorithms
- Bring your own script
- Bring your own container
- SageMaker Experiments
- Lab 5: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
- SageMaker Debugger
- Lab 6: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
- Automatic model tuning
- SageMaker Autopilot: Automated ML
- Bias detection
- Lab 7: Using SageMaker Clarify for Bias
- 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
- Lab 8: Inferencing with SageMaker Studio
- Amazon SageMaker Model Monitor
- Lab 9: Model Monitoring
- 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
- 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
- Challenge 7: Automate full model development process using SageMaker Pipeline
