Google Professional Machine Learning Engineer
Validate your ability to build, evaluate, productionize, and optimize ML and AI solutions on Google Cloud. Covers architecting low-code AI solutions with BigQuery ML and AutoML, managing data and models across teams, scaling prototypes into production ML models, serving and scaling models for real-time and batch inference, automating and orchestrating ML pipelines with Vertex AI, and monitoring AI solutions for drift, fairness, and performance. The 2026 exam reflects the transition to Gemini Enterprise Agent Platform and emphasizes generative AI, Model Garden, Vertex AI Agent Builder, and responsible AI practices. Recommended experience is 3+ years in industry including 1+ year designing and managing solutions on Google Cloud. The exam uses pass/fail scoring (no numeric score disclosed); the 70/100 threshold shown is a widely cited community estimate.
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