Machine Learning Engineer
The path to senior ML engineering roles on AWS. Builds from cloud fundamentals through the ML Engineer Associate and into the Machine Learning Specialty — the top AWS credential for practitioners who design and deploy ML systems at scale.
CLF-C02 establishes AWS fundamentals before you tackle machine learning services. Understanding core AWS infrastructure is essential before building ML pipelines on SageMaker.
SAA-C03 provides the AWS architecture knowledge needed to design ML infrastructure. Compute, storage, networking, and IAM concepts from SAA-C03 directly underpin ML pipeline design.
MLA-C01 validates hands-on ML engineering skills on AWS — model training, tuning, deployment, and MLOps pipelines using SageMaker. The required stepping stone before the Machine Learning Specialty.
MLS-C01 is the senior ML credential on AWS. It validates expert-level ability to design and implement ML solutions at scale — commanding $150,000–$190,000 for certified ML specialists.