🤖 Google Professional ML Engineer

An independent, free exam-preparation course that walks through the publicly published Google Cloud Professional Machine Learning Engineer (PMLE) exam guide. It covers the six official domains — framing ML problems, architecting ML solutions, designing data preparation and processing systems, developing ML models, automating and orchestrating ML pipelines, and monitoring, optimizing and maintaining ML solutions — built around Google Cloud's ML stack: Vertex AI (AutoML, custom training, pipelines, Feature Store, Model Registry), BigQuery ML, TensorFlow/Keras, model deployment and serving, MLOps and CI/CD, monitoring and drift detection, responsible AI and fairness, and generative AI on Vertex. It uses visual lessons, worked examples, original self-check questions and a 45-question final exam to build exam-ready understanding. It is awareness/prep only — not the official Google Cloud training or the proctored certification exam — and claims no Google affiliation or endorsement. The course is organized into 22 modules, ending with a final exam (pass mark 80%). It is independent, free exam-preparation training — not an official or accredited review course.

What you'll learn

• Framing ML Problems
• Architecting ML Solutions on Google Cloud
• Data Ingestion & Preparation
• Feature Engineering & Vertex AI Feature Store
• BigQuery ML
• TensorFlow & Keras Fundamentals
• Model Training on Vertex AI
• Model Evaluation & Metrics
• Hyperparameter Tuning
• Vertex AI AutoML
• Model Deployment & Serving
• Model Registry & Versioning
• Vertex AI Pipelines & Orchestration
• MLOps & CI/CD for ML
• Model Monitoring & Drift Detection
• Responsible AI & Fairness
• Explainable AI
• Generative AI on Vertex
• Security, Privacy & ML Governance
• Notebooks, Workbench & Experiments
• Cost, Latency & Optimization
• Exam Strategy & Final Review

Learning objectives

• Frame business problems as well-posed ML problems and judge when ML is (and is not) the right tool.
• Architect end-to-end ML solutions on Google Cloud, choosing between AutoML, BigQuery ML and custom training.
• Design data ingestion, preparation and feature-engineering pipelines with Dataflow, BigQuery and Vertex AI Feature Store.
• Train, evaluate and tune models, including hyperparameter tuning and distributed training on Vertex AI.
• Build with BigQuery ML and TensorFlow/Keras, understanding when each fits the use case.
• Deploy and serve models with Vertex AI endpoints, batch prediction and online prediction.
• Automate and orchestrate ML pipelines with Vertex AI Pipelines, Kubeflow and CI/CD (MLOps).
• Monitor models in production for skew, drift and performance, and trigger retraining.
• Apply responsible AI, fairness, explainability and security practices across the lifecycle.
• Use generative AI on Vertex (Model Garden, Gemini, tuning, grounding) and apply exam strategy.
• Pass a 45-question exam (80% to earn your certificate).