☁️ AWS Certified Machine Learning Engineer - Associate (MLA-C01) - Exam-Prep

For data scientists, ML practitioners, and cloud engineers preparing to build, deploy, and operate production ML workloads on AWS and pass the MLA-C01 exam. The course is organized into 8 modules, ending with a final exam (pass mark 70%). It is independent, free exam-preparation training — not an official or accredited review course.

What you'll learn

• Domain 1.1 — Data Ingestion and Storage for ML
• Data Transformation, Feature Engineering & Integrity
• Choosing the Modeling Approach: Algorithm and Model Selection
• Model Training, Tuning, and Evaluation
• Selecting Deployment Infrastructure & Model Serving
• ML Pipeline Automation, Orchestration & CI/CD (MLOps)
• Monitoring Models, Data Drift, and Infrastructure
• Securing, Governing, and Cost-Optimizing ML Solutions

Learning objectives

• Ingest, transform, and engineer features from data using AWS services (S3, Glue, Athena, EMR, SageMaker Data Wrangler / Feature Store).
• Select, train, tune, and evaluate ML models with Amazon SageMaker, including built-in algorithms, hyperparameter tuning, and bias detection.
• Choose appropriate model deployment targets (real-time, serverless, asynchronous, batch transform) and serving infrastructure.
• Build and automate end-to-end ML pipelines using SageMaker Pipelines, Step Functions, EventBridge, and CI/CD practices (MLOps).
• Monitor models and data for drift, quality, and performance using SageMaker Model Monitor and Clarify.
• Secure ML systems with IAM, VPC, encryption (KMS), and apply governance, lineage, and compliance controls.
• Apply cost-optimization strategies across the ML lifecycle (Spot instances, right-sizing, serverless inference).
• Map exam scenarios to the right AWS service and answer MLA-C01-style questions with confidence.