MLA-C01 (AWS Certified Machine Learning- Associate)

• Evaluating ML business challenges
• ML training approaches
• ML training algorithms

• Data preparation and types
• Exploratory data analysis
• AWS storage options and choosing storage

• Handling incorrect, duplicated, and missing data
• Feature engineering concepts
• Feature selection techniques
• AWS data transformation services

• Amazon SageMaker AI built-in algorithms
• Selecting built-in training algorithms
• Amazon SageMaker Autopilot
• Model selection consideration
• ML cost considerations

• Model training concepts
• Training models in Amazon SageMaker AI

• Evaluating model performance
• Techniques to reduce training time
• Hyperparameter tuning techniques

• Deployment considerations and target options
• Deployment strategies
• Choosing a model inference strategy
• Container and instance types for inference

• Access control
• Network access controls for ML resources
• Security considerations for CI/CD pipelines

• Introduction to MLOps
• Automating testing in CI/CD pipelines
• Continuous delivery services

• Detecting drift in ML models
• SageMaker Model Monitor
• Monitoring for data quality and model quality
• Automated remediation and troubleshooting