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Google Cloud Professional Machine Learning Engineer training and exam prep - build and deploy ML and generative-AI solutions with Vertex AI.
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The Professional Machine Learning Engineer proves you can design, build, deploy and operate machine-learning and generative-AI solutions on Google Cloud. It covers architecting low-code and custom ML solutions; collaborating to manage data and models; scaling prototypes into production models; serving and scaling models; automating and orchestrating ML pipelines (MLOps); and monitoring ML solutions - primarily with Vertex AI and BigQuery ML. The exam was revised in 2026 to include generative AI, such as Vertex AI and Gemini. The exam has 50-60 questions (multiple-choice and multiple-select) in 120 minutes. It is graded pass/fail - Google does not publish a numeric passing score. It costs about US$200 and is taken online with remote proctoring or at a test centre, delivered through Pearson VUE. You register through Google's certification portal. The certification is valid for two years; you renew by retaking the exam.
The Professional Machine Learning Engineer proves you can build and deploy ML and generative-AI systems on Google Cloud - among the most sought-after skills in tech. It supports machine-learning engineer, AI engineer and data scientist roles, all in very strong demand as organisations adopt AI and generative AI.
Source: Glassdoor
Source: Indeed
Annual Salary
Source: Glassdoor
Hiring Companies
Source: Indeed
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This is the machine-learning and AI certification for Google Cloud, ideal for: - Machine-learning engineers and AI engineers on Google Cloud - Data scientists building and deploying models - Software engineers moving into ML/AI and generative AI Google recommends about 3+ years of industry experience including 1+ year on Google Cloud, plus comfort with ML concepts and Python. If you are new to Google Cloud, take the Associate Cloud Engineer first; if your focus is data pipelines, see the Professional Data Engineer.
you are new to Google Cloud, take the Associate Cloud Engineer first; if your focus is data pipelines, see the Professional Data Engineer. How to earn it:
1. Prepare with a Professional Machine Learning Engineer exam-prep course (self-paced, online) - that is what we provide.
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The Google Cloud Professional Machine Learning Engineer certification validates your ability to design, build, deploy, and manage machine learning solutions on Google Cloud. This professional credential demonstrates your expertise in machine learning model development, data preparation, deployment, monitoring, responsible AI, and ML operations. This section covers everything you need to know about the Professional Machine Learning Engineer exam, including the exam format, eligibility requirements, certification process, passing criteria, certification validity, and frequently asked questions, helping you prepare with confidence.
Exam details verified on 6 August 2026. Google grades this exam pass/fail and does not publish a numeric passing score, so ignore any site claiming a specific percentage as official. The exam was revised in 2026 to include generative AI. You register through Google's certification portal, and the exam is delivered through Pearson VUE. Confirm current details at cloud.google.com/learn/certification/machine-learning- engineer.
This is the machine-learning and AI certification for Google Cloud, ideal for: - Machine-learning engineers and AI engineers on Google Cloud - Data scientists building and deploying models - Software engineers moving into ML/AI and generative AI
