Databricks · on Databricks Academy
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.
Working professionals and career changers. This is an intermediate certification in Professional Certifications, running about Exam: 90 minutes, 48 scored questions.
$200 exam fee. Registration fee $200; no prerequisites but related training recommended. 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.
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.