Google Cloud Professional Machine Learning Engineer Cert Prep: 4 Developing ML Models

Learn about developing machine learning models in preparation for the Google Professional Machine Learning Engineer certification exam.

Introduction

• Overview
• Course four key terminology

1. Building Models

• Using TensorFlow Playground
• Overfitting vs. underfitting
• Selecting the right metrics

2. Training Models

• Training models with TensorFlow GPU-enabled Docker
• Fine-tuning raw ingredients Hugging Face
• Advantages transfer learning

3. Scaling Model Training and Serving

• 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

Conclusion

• Next steps