📈 AWS Machine Learning Engineer – Associate (MLA-C01)

An independent, free exam-preparation course that walks through the publicly published AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam guide and its four content domains — Data Preparation for ML (28%), ML Model Development (26%), Deployment & Orchestration of ML Workflows (22%), and ML Solution Monitoring, Maintenance & Security (24%). It uses visual lessons, worked examples, original self-check questions and a 45-question final exam to build exam-ready understanding of data ingestion on Amazon S3, AWS Glue and SageMaker Feature Store, feature engineering and bias handling, model training and tuning, SageMaker built-in algorithms, Autopilot and JumpStart, evaluation metrics, real-time / batch / serverless deployment, SageMaker Pipelines and MLOps, Model Monitor and Clarify drift detection, security and governance, and cost optimisation. It is awareness/prep only — not an official AWS training course or the certification exam — and claims no AWS affiliation or endorsement. The course is organized into 22 modules, ending with a final exam (pass mark 80%). It is independent, free exam-preparation training — not an official or accredited review course.

What you'll learn

• The MLA-C01 Certification, Role & Exam Structure
• Data Ingestion & Storage on Amazon S3
• AWS Glue, Data Catalog & Athena
• SageMaker Feature Store
• Data Transformation & Feature Engineering
• Class Imbalance, Bias & Data Quality
• Data Labeling — SageMaker Ground Truth
• ML Problem Framing & Algorithm Selection
• SageMaker Built-in Algorithms
• Training Jobs, Script Mode & Distributed Training
• Automatic Model Tuning (Hyperparameters)
• SageMaker Autopilot & JumpStart
• Model Evaluation Metrics & Overfitting
• Real-time Endpoints & Serverless Inference
• Batch & Asynchronous Inference
• MLOps & SageMaker Pipelines
• Monitoring & Drift — SageMaker Model Monitor
• Bias & Explainability — SageMaker Clarify
• Security, IAM & Governance
• Cost Optimisation for ML Workloads
• Supporting AWS AI Services & SageMaker Studio
• Exam Strategy, Question Tactics & Next Steps

Learning objectives

• Understand that this is independent MLA-C01 exam-prep over the public AWS exam guide, not an official AWS course or the certification exam
• Describe the AWS ML Engineer – Associate role and the four content domains with their weightings
• Ingest, store and catalog data with Amazon S3, AWS Glue, Glue Data Catalog, Athena and SageMaker Feature Store
• Transform data and engineer features — scaling, encoding, imputation, splitting — with Data Wrangler and Processing jobs
• Detect and mitigate class imbalance and bias, and validate data quality before modelling
• Train models with SageMaker built-in algorithms, training jobs, script mode and distributed training
• Tune hyperparameters with Automatic Model Tuning, and accelerate with Autopilot and JumpStart
• Evaluate models with the right metrics (accuracy, precision/recall, F1, AUC, RMSE) and avoid over/under-fitting
• Deploy to real-time endpoints, batch transform, asynchronous and serverless inference, and automate with SageMaker Pipelines, Model Registry, Model Monitor, Clarify, IAM security and cost controls
• Pass a 45-question exam (80% to earn your certificate)