Professional Machine Learning Engineer Certification
Google Skills
Last updated June 16, 2026
A comprehensive, role-based path — around 99 hours — for engineers who design, build, and productionize machine-learning systems on Google Cloud. Through on-demand courses, labs, and skill badges it spans the full lifecycle: preparing data, building and training models with BigQuery ML and Keras, production ML systems and MLOps with Vertex AI, generative AI, and responsible AI. Concept material is free; the labs and skill badges run on Google Skills and need a subscription or credits. It assumes you can code.
What you'll learn
- Preparing data and engineering features for predictive modeling
- Building and training ML models with BigQuery ML and Keras
- Production machine-learning systems and getting started with MLOps
- Managing features, model evaluation, and MLOps on Vertex AI
- Building generative-AI applications and MLOps for generative AI on Google Cloud
- Responsible AI for developers: fairness and bias, interpretability and transparency, privacy and safety
Frequently asked questions about Professional Machine Learning Engineer Certification
Who is Professional Machine Learning Engineer Certification for?
For experienced engineers who can code and want a comprehensive, hands-on path to designing and operating production machine-learning systems on Google Cloud.
What are the prerequisites for Professional Machine Learning Engineer Certification?
An engineering background and the ability to code (e.g. Python, SQL). A comprehensive, role-based path (around 99 hours).
Do you need to code for Professional Machine Learning Engineer Certification?
Yes — Professional Machine Learning Engineer Certification involves hands-on coding.
Why we suggest this course
This is the deepest end-to-end machine-learning engineering path in the catalog, covering classic ML, MLOps, generative AI, and responsible AI in one applied, hands-on sequence built around Google Cloud tooling. Two things worth knowing: the title includes "Certification" because the path doubles as study preparation for Google Cloud's standalone, paid Professional Machine Learning Engineer exam — completing the path earns Google skill badges and the underlying skills, not the exam credential, which is sat and paid for separately; and at roughly 99 hours it is a major commitment.