Google Cloud Professional Machine Learning Engineer Cert Prep: 4 Developing ML Models
1h 30mAdvanced2023-06-16
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
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.
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.
Skills covered
Google CloudMachine LearningSoftware Development ToolsGoogleCloud PlatformsCert PrepArtificial Intelligence (AI)Cloud ComputingSoftware Development
Concepts
0. Introduction
- 01 - Overview
- 02 - Course four key terminology
1. Building Models
- 03 - Using TensorFlow Playground
- 04 - Overfitting vs. underfitting
- 05 - Selecting the right metrics
2. Training Models
- 06 - Training models with TensorFlow GPU-enabled Docker
- 07 - Fine-tuning raw ingredients Hugging Face
- 08 - Advantages transfer learning
3. Scaling Model Training and Serving
- 09 - Operationalize microservices
- 10 - Monitoring and logging with Rust on Google App Engine
- 11 - Continuous integration Rust with GitHub Actions
- 12 - Demo - Unit testing Rust
- 13 - Demo - GitHub copilot-enabled Rust
- 14 - Setup GCP workstation with Python
- 15 - Demo - Google Cloud Shell
- 16 - Demo - Google Cloud Editor
- 17 - Demo - Google CLI SDK
- 18 - Demo - Google gcloud CLI
- 19 - Demo - Google App Engine Rust Deploy
- 20 - Demo - Google App Engine Golang
Conclusion
- 21 - Next steps