AWS Certified Machine Learning Engineer – Associate
Associate AWS ML engineering program covering SageMaker, feature engineering, model deployment, MLOps pipelines, monitoring, and production ML operations—aligned to the AWS Certified Machine Learning Engineer – Associate exam.
4.8
(480 students)
8 Weeks · 50 hours
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What is this course about?
AWS Certified Machine Learning Engineer – Associate
MLA-C01 · Production ML on Amazon SageMaker
Learn to implement, deploy, and operate ML workloads on AWS—feature pipelines, training/tuning, endpoints, SageMaker Pipelines, and Model Monitor—mapped to the MLA-C01 associate exam.
What you'll walk away with:
- End-to-end SageMaker workflows from data prep through deployment
- Feature Store and preprocessing patterns that hold up in production
- Real-time and batch inference with safe rollout options
- MLOps automation with pipelines, Model Registry, and drift monitoring
- MLA-C01 domain practice and a capstone operational ML workload
Course Features
Post Graduate Diploma
8 Weeks of Content
Hands-on Projects
Community Support
Lifetime Access
Student Reviews
Rohit Malhotra
Feature Store + Pipelines labs mirror what our MLOps team actually ships. Model Monitor drift lab saved us weeks of guessing in production.
Flipkart Data PlatformSophie Tran
HPO and endpoint scaling labs were exactly MLA-C01 depth. Capstone forced a full train→registry→deploy path, not notebook demos.
Singapore ML PlatformVikram Shah
Finally learned SageMaker the way production engineers use it—script mode, batch transform, and pipeline approvals included.
Accenture AWS PracticeJulia Bergmann
Clear path from features to monitoring. Practice questions mapped cleanly to exam domains after the labs.
Berlin Mobility AI
