AWS Certified Machine Learning Engineer - Associate

Published weight 28%. Ingesting and storing data, transforming data and performing feature engineering, and ensuring data integrity and preparing data for modeling (bias detection, encryption, compliance).

• Ingest and store data
• Transform data and perform feature engineering
• Ensure data integrity and prepare data for modeling

Published weight 26%. Choosing a modeling approach (algorithms, AI services, SageMaker built-ins), training and refining models (hyperparameter tuning, regularization, versioning), and analyzing model performance (metrics, bias, debugging).

• Choose a modeling approach
• Train and refine models
• Analyze model performance

Published weight 22%. Selecting deployment infrastructure (endpoint types, compute), creating and scripting infrastructure (IaC, containers, auto scaling), and using orchestration tools for CI/CD pipelines.

• Select deployment infrastructure based on existing architecture and requirements
• Create and script infrastructure based on existing architecture and requirements
• Use automated orchestration tools to set up CI/CD pipelines

Published weight 24%. Monitoring model inference (drift, data quality, A/B testing), monitoring and optimizing infrastructure and costs, and securing AWS resources (IAM least privilege, network isolation, auditing).

• Monitor model inference
• Monitor and optimize infrastructure and costs
• Secure AWS resources