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AWS Certified Machine Learning Engineer – Associate
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Tuesday, June 23
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Ingesting & storing data for ML
Transforming & cleaning data
Feature engineering & the Feature Store
Splitting, balancing & data integrity
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Data Preparation: Ingesting, cleaning, transforming, engineering, splitting and storing data so it is ready to train ML models on AWS (Domain 1 · 28%)
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Model Development: Choosing approaches and algorithms, training and tuning models in SageMaker, evaluating them honestly, and detecting bias (Domain 2 · 26%)
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Deployment & Orchestration: Serving models with the right endpoint type, managing model versions, and automating ML workflows with pipelines and CI/CD (Domain 3 · 22%)
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Monitoring & Security: Monitoring models and infrastructure, detecting drift and retraining, securing ML solutions with IAM/VPC/KMS, and optimising cost (Domain 4 · 24%)
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Ingesting & storing data for ML
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