Google Cloud Certified - Professional Machine Learning Engineer
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.
The Professional Machine Learning Engineer exam assesses your ability to:
- Frame ML problems
- Develop ML models
- Architect ML solutions
- Automate and orchestrate ML pipelines
- Design data preparation and processing systems
- Monitor, optimize, and maintain ML solutions
About this certification exam
Length: 2 hours
Registration fee: $200 (plus tax where applicable)
Languages: English
Exam format: Multiple choice and multiple select, taken remotely or in person at a test center. Locate a test center near you.
Exam delivery method:
a. Take the online-proctored exam from a remote location, review the online testing requirements
b. Take the onsite-proctored exam at a testing center, locate a test center near you
Prerequisites: None
Recommended experience: 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud
Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date of certification. Recertification is accomplished by retaking the exam during the recertification eligibility time period and achieving a passing score. You may attempt recertification starting 60 days prior to your certification expiration date.
For Google Cloud Certified – Professional Machine Learning Engineer, the role reflects evolving demands in AI-driven and data-centric cloud environments:
Increased reliance on machine learning, AI solutions, and data-driven decision-making
Strong focus on designing, building, training, deploying, and maintaining scalable ML models
Core skills include machine learning pipelines, data preparation, model optimization, security, and automation
Working with Google Cloud AI and ML services, understanding their use cases, and ensuring model performance and reliability
Machine Learning Engineers must decide when to build custom ML models or use managed AI services to improve efficiency, scalability, and business impact
