Professional ML Engineer Exam Guide | Certification | Google Cloud | Learn
Chapter 1 - Framing ML Problems
Chapter 2 - Exploring Data and Building Data Pipelines
Chapter 3 - Feature Engineering
Chapter 4 - Choosing the right ML instrastructure
Chapter 5 - Architecting ML Solutions
Chapter 6 - Building Secure ML Pipelines
Chapter 8 - Model Training and Hyperparameter Tuning
Chapter 9 - Model Explainability on Vertex AI
Chapter 10 - Scaling Models in Production
Chapter 11 - Designing ML Training Pipelines
Chapter 12 - Model Monitoring, Tracking and Auditing Metadata
Chapter 13 - Maintaining ML Solutions