Google Cloud Certified – Professional Machine Learning Engineer
 
Course Description
Google Cloud Certified – Professional Machine Learning Engineer is an advanced, hands-on training program designed to help ML practitioners, data scientists, and cloud engineers build, deploy, and optimize machine learning solutions on Google Cloud. This course prepares you to pass the Professional Machine Learning Engineer certification while developing real-world expertise in designing scalable, production-ready ML systems.
 
You’ll learn how to frame ML problems, select optimal data strategies, build reliable pipelines, train and evaluate models, and deploy them using services such as Vertex AI, BigQuery ML, AutoML, and other GCP tools. The course also covers MLOps workflows, feature engineering, monitoring, fairness and interpretability, and end-to-end ML lifecycle management following Google Cloud best practices.
 
By working through practical labs and real-world scenarios, you’ll gain the technical depth needed to architect, automate, and operate machine learning solutions that perform reliably at scale.
 
What You’ll Learn
ML problem framing, data preparation, and feature engineering
Building, training, and evaluating models on Vertex AI and AutoML
Using BigQuery ML for SQL-based model development
Designing scalable ML pipelines with Kubeflow, Dataflow, and Cloud Composer
ML deployment patterns, CI/CD workflows, and model serving options
Monitoring deployed models for drift, performance, and fairness
Applying responsible AI principles: interpretability, bias mitigation, and ethical design
Best practices aligned with the Professional Machine Learning Engineer exam
 
Who This Course Is For
Machine learning engineers and data scientists
Cloud engineers implementing ML solutions on Google Cloud
Developers transitioning into applied ML roles
Professionals preparing for the Google Cloud Professional ML Engineer certification
Anyone building end-to-end production ML systems on GCP