Amazon Web Services · on AWS Training
Validates the ability to build, deploy, monitor and operationalize ML models on AWS using SageMaker and related services; AWS recommends at least a year of hands-on experience. The main AWS credential for ML engineers and data scientists now that the ML Specialty exam has retired.
Working professionals and career changers. This is an intermediate certification in Professional Certifications, running about Exam: 130 minutes, 65 questions (MLA-C01).
$150 exam fee (MLA-C01); $75 beta pricing for updated MLA-C02. Current MLA-C01 exam is 150 USD; the updated MLA-C02 beta is listed at 75 USD. The older ML - Specialty exam was retired March 31, 2026. Seen 2026-11-01.
Validates using Databricks and MLflow to perform basic ML tasks including data exploration, feature engineering, model training, tuning and deployment. A practical credential for data scientists and engineers working in Databricks-based lakehouse environments.
A proctored exam on using GitHub Copilot responsibly, its features across plans, data and architecture, prompt engineering and context crafting, developer productivity, and privacy and content-exclusion safeguards. For developers and tech leads who want a recognised credential showing they can use an AI coding assistant well.
A multi-course IBM program covering machine learning with Python, deep learning with Keras and PyTorch, and generative AI and LLM engineering, finishing with a capstone. Good for developers and data analysts who want a hands-on, portfolio-building AI engineering credential.