☁️ Google Cloud Professional ML Engineer (PMLE) - Exam-Prep

For ML practitioners and cloud engineers who want to design, build, deploy, and operate production ML systems on Google Cloud and pass the PMLE exam with confidence. The course is organized into 8 modules, ending with a final exam (pass mark 70%). It is independent, free exam-preparation training — not an official or accredited review course.

What you'll learn

• Exam Overview, Domains & ML Problem Framing on Google Cloud
• Architecting Low-Code AI Solutions: BigQuery ML, AutoML, Gemini & Pre-built APIs
• Collaborate Across Teams to Manage Data & Models
• Scaling Prototypes into Production ML: Custom & Distributed Training and Hyperparameter Tuning
• Serve & Scale Models: Endpoints, Batch Prediction, Hardware & Cost
• Automate & Orchestrate ML Pipelines: Vertex AI Pipelines, CI/CD & Metadata
• Monitor AI Solutions: Skew & Drift, Performance, and Responsible AI
• Exam Strategy, Mock Exam & Final Review

Learning objectives

• Map every PMLE exam domain to concrete Google Cloud services and choose the right service for each ML task.
• Architect low-code and pre-built AI solutions using Vertex AI, BigQuery ML, and Gemini-based APIs.
• Collaborate across teams to manage data, features, and model artifacts with Vertex AI Feature Store, datasets, and Model Registry.
• Scale prototypes into trainable models using custom training, distributed training, hyperparameter tuning, and AutoML.
• Serve and scale models with Vertex AI Endpoints, batch prediction, optimized hardware, and cost/latency trade-offs.
• Automate and orchestrate end-to-end ML pipelines with Vertex AI Pipelines, CI/CD, and metadata tracking.
• Monitor deployed AI solutions for skew, drift, performance, and responsible-AI concerns, and trigger retraining.
• Apply exam-taking strategy to scenario-based multiple-choice and multiple-select questions under time pressure.