Professional PMLE
Top-tier GCP ML cert for engineers designing, building, and productionizing ML models using Vertex AI, TensorFlow, and MLOps best practices on Google Cloud.
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The Google Cloud Professional Machine Learning Engineer certification validates the ability to design, build, and productionize ML models using Google Cloud technologies. It targets ML engineers who manage the full ML lifecycle on GCP — from data preparation through model training, evaluation, deployment, and monitoring in production.
The exam covers five areas: Architect Low-Code ML Solutions (12%), Collaborate within and Across Teams to Manage Data and Models (16%), Scale Prototypes into ML Models (18%), Serve and Scale Models (20%), and Automate and Orchestrate ML Pipelines (22%), plus Monitor ML Solutions (12%). Core services include Vertex AI (AutoML, Workbench, Feature Store, Model Registry, Endpoint, Pipelines, Model Monitoring), BigQuery ML, TensorFlow Extended (TFX), Kubeflow Pipelines, Cloud Build for ML CI/CD, and Explainable AI.
The exam is valid for 2 years with approximately 50 questions in 120 minutes. It is one of the most rigorous ML certifications in the cloud space, requiring both theoretical ML knowledge and practical GCP MLOps expertise. Combined with the Professional Data Engineer, it represents a complete Google Cloud data and AI engineering credential suite.
“PMLE was a challenge and this made it manageable. Models, Vertex AI and MLOps all covered. Good score. 🤖”
“Best PMLE bank I've used. The explanations cleared up several advanced ML ideas, and I could focus on my weak domains rather than revising everything.”
Every PMLE question is written by someone who holds this certification and works in the field — not scraped from other sites, and not generated and shipped unreviewed. Every explanation you are shown has been read and approved by that same expert first.
ML engineers, data scientists, and AI platform engineers building and operating ML models in production on Google Cloud.
Prerequisites: 3+ years of ML engineering experience and 1+ year with Vertex AI or equivalent GCP services. Python proficiency and ML framework experience required.