Learn about developing machine learning models in preparation for the Google Professional Machine Learning Engineer certification exam.
Overview
Syllabus
Introduction
- Overview
- Course four key terminology
- Using TensorFlow Playground
- Overfitting vs. underfitting
- Selecting the right metrics
- Training models with TensorFlow GPU-enabled Docker
- Fine-tuning raw ingredients Hugging Face
- Advantages transfer learning
- Operationalize microservices
- Monitoring and logging with Rust on Google App Engine
- Continuous integration Rust with GitHub Actions
- Demo: Unit testing Rust
- Demo: GitHub copilot-enabled Rust
- Setup GCP workstation with Python
- Demo: Google Cloud Shell
- Demo: Google Cloud Editor
- Demo: Google CLI SDK
- Demo: Google gcloud CLI
- Demo: Google App Engine Rust Deploy
- Demo: Google App Engine Golang
- Next steps
Taught by
Noah Gift