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

سرفصل های دوره

The Google Professional Machine Learning Engineer certification lets prospective employers know that you have the knowledge to design, build, and productionize machine learning models to solve business challenges using Google Cloud technologies, and knowledge of proven ML models and techniques.

In this fourth course in the certification prep series, instructor Noah Gift covers topics relating to developing machine learning models. He shows you how to build models using TensorFlow, explains the concepts of overfitting versus underfitting, and how to select the right evaluation metrics. Noah then explains how to train modes, including the advantages of transfer learning. Finally, learn about scaling model training and serving, including how to build a microservice and deploy it with Rust on Google App Engine.


01 - Introduction
  • 01 - Overview
  • 02 - Course four key terminology

  • 02 - 1. Building Models
  • 01 - Using TensorFlow Playground
  • 02 - Overfitting vs. underfitting
  • 03 - Selecting the right metrics

  • 03 - 2. Training Models
  • 01 - Training models with TensorFlow GPU-enabled Docker
  • 02 - Fine-tuning raw ingredients Hugging Face
  • 03 - Advantages transfer learning

  • 04 - 3. Scaling Model Training and Serving
  • 01 - Operationalize microservices
  • 02 - Monitoring and logging with Rust on Google App Engine
  • 03 - Continuous integration Rust with GitHub Actions
  • 04 - Demo Unit testing Rust
  • 05 - Demo GitHub copilot-enabled Rust
  • 06 - Setup GCP workstation with Python
  • 07 - Demo Google Cloud Shell
  • 08 - Demo Google Cloud Editor
  • 09 - Demo Google CLI SDK
  • 10 - Demo Google gcloud CLI
  • 11 - Demo Google App Engine Rust Deploy
  • 12 - Demo Google App Engine Golang

  • 05 - Conclusion
  • 01 - Next steps
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