Google Cloud Professional Machine Learning Engineer
This exam validates designing and operationalizing ML solutions on Google Cloud: architecting low-code and custom solutions, managing data and models across teams, scaling prototypes into models, serving and scaling models with Vertex AI, automating ML pipelines, and monitoring deployed AI solutions for performance and fairness.
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Start Professional ML Engineer examSkills measured
Architecting low-code AI solutions
13%BigQuery ML, pre-trained APIs, AutoML, and choosing build vs buy.
Collaborating to manage data and models
14%Feature Store, dataset management, experiment tracking, and responsible AI.
Scaling prototypes into ML models
18%Framework selection, distributed training, hardware (TPU/GPU), and hyperparameter tuning.
Serving and scaling models
20%Vertex AI endpoints, batch prediction, latency and throughput, and A/B rollout.
Automating and orchestrating ML pipelines
22%Vertex AI Pipelines, CI/CD for ML, and retraining triggers.
Monitoring AI solutions
13%Skew and drift detection, performance monitoring, and troubleshooting.