IBM · on Coursera
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.
Working professionals and career changers. This is an intermediate course in Professional Certifications, running about 13 courses, approx. 4 months at 10 hrs/week.
Included with Coursera Plus ($49/month); free to audit. Page shows 'Enroll for free' and 'Included with Coursera Plus'; financial aid available. Seen 2026-11-01.
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.
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.