Google Cloud Platform for Machine Learning Essential Training

Learn how to design machine learning solutions with Google Cloud Platform.

Introduction

• GCP and Machine Learning
• What you should know
• About using cloud services

1. Vertex AI Studio

• Use Vertex AI Model Garden
• Design and test language model prompts
• Design and test multimodal model prompts
• Test image model generative output
• Design and test speech generative output
• Challenge: Select and test GenAI models
• Solution: Select and test GenAI models

2. Vertex AI Notebooks

• Understand available services
• Use TensorFlow example: MNIST
• Use managed and user-managed notebooks
• Update notebook instance
• Use notebook instances
• Challenge: Setup notebook
• Solution: Setup notebook

3. Model Development

• Understand Vector Search
• Use Vector Search
• Understand Feature Store
• Challenge: Create a Feature Store
• Solution: Create a Feature Store

4. Model Deployment

• Use the model registry
• Register a model in the registry
• Review batch and online endpoints
• Understand model pipeline templates
• Challenge: Run and evaluate a model pipeline job
• Solution: Run and evaluate a model pipeline job

Conclusion

• Next steps