Curriculum
- 3 Sections
- 12 Lessons
- 3 Days
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- Day 14
- 1.1Module 1: Introduction to Machine Learning (ML) on AWS Introduction to ML Amazon SageMaker AI Responsible ML
- 1.2Module 2: Analyzing Machine Learning (ML) Challenges Evaluating ML business challenges ML training approaches ML training algorithms
- 1.3Module 3: Data Processing for Machine Learning (ML) Data preparation and types Exploratory data analysis AWS storage options and choosing storage
- 1.4Module 4: Data Transformation and Feature Engineering Handling incorrect, duplicated, and missing data Feature engineering concepts Feature selection techniques AWS data transformation services Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK
- Day 24
- 2.1Module 5: Choosing a Modeling Approach Amazon SageMaker AI built-in algorithms Selecting built-in training algorithms Amazon SageMaker Autopilot Model selection considerations ML cost considerations
- 2.2Module 6: Training Machine Learning (ML) Models Model training concepts Training models in Amazon SageMaker AI Lab 3: Training a model with Amazon SageMaker AI
- 2.3Module 7: Evaluating and Tuning Machine Learning (ML) models Evaluating model performance Techniques to reduce training time Hyperparameter tuning techniques Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI
- 2.4Module 8: Model Deployment Strategies Deployment considerations and target options Deployment strategies Choosing a model inference strategy Container and instance types for inference Lab 5: Shifting Traffic A/B
- Day 34
- 3.1Module 9: Securing AWS Machine Learning (ML) Resources Access control Network access controls for ML resources Security considerations for CI/CD pipelines
- 3.2Module 10: Machine Learning Operations (MLOps) and Automated Deployment Introduction to MLOps Automating testing in CI/CD pipelines Continuous delivery services Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio
- 3.3Module 11: Monitoring Model Performance and Data Quality Detecting drift in ML models SageMaker Model Monitor Monitoring for data quality and model quality Automated remediation and troubleshooting Lab 7: Monitoring a Model for Data Drift
- 3.4Module 12: Course Wrap-up
Module 4: Data Transformation and Feature Engineering Handling incorrect, duplicated, and missing data Feature engineering concepts Feature selection techniques AWS data transformation services Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK
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Module 6: Training Machine Learning (ML) Models Model training concepts Training models in Amazon SageMaker AI Lab 3: Training a model with Amazon SageMaker AI
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