- Prior completion “Machine Learning on Google Cloud” course or the equivalent knowledge of TensorFlow/Keras and machine learning.
- Experience scripting in Python and working in Jupyter notebooks to create machine learning models.
- Understand the model options available within Vertex AI Model Garden
- Incorporate models in Vertex AI Model Garden in your machine learning workflows
- Leverage foundation models for generative AI use cases
- Fine-tune models to meet your specific needs
Who Can Benefit?
Machine learning practitioners who wish to leverage models available in Vertex AI Model Garden for various different use cases.
Outline for Vertex AI Model Garden Training
Vertex AI for ML Workloads
- Vertex AI on Google Cloud
- Options for training, tuning and deploying ML models on Vertex AI
- Generative AI options on Google Cloud and Vertex AI
- Introduction to Model Garden
- Model types in Model Garden
- Connecting models from Gen AI Studio and Model Registry
- Introduction to course use cases
Task-specific Solutions: Content Classification
- Pre-trained models for specific tasks
- VertexAI AutoML
- Using a pre-trained model via the Python SDK
- Lab: Content Classification via Natural Language API and AutoML
Foundation Models: Text Embeddings via PaLM
- Introduction to foundation models
- PaLM API
- GenAI Studio
- Using the Embeddings API
- Lab: Use the PaLM API to Cluster Products Based on Descriptions
- Fine-tunable models in Model Garden
- Vertex AI Pipelines
- Demo: Fine-tuning models for your specific use case