Course Overview
Vertex AI Model Garden provides enterprise-ready foundation models, task-specific models, and APIs. Model Garden can serve as the starting point for model discovery for various different use cases. You can kick off a variety of workflows including using models directly, tuning models in Generative AI Studio, or deploying models to a data science notebook.
In this class, after being introduced to Vertex AI as a machine learning platform through the lens of Model Garden. You will learn how to leverage re-trained models as part of your machine learning workflow and how to fine-tune models for your specific applications.
Who should attend
Machine learning practitioners who wish to leverage models available in Vertex AI Model Garden for various different use cases.
Prerequisites
To get the most out of this course, participants should have:
- Prior completion of Machine Learning on Google Cloud (MLGC) 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.
Course Objectives
- Understanding 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
Outline: Vertex AI Model Garden (VAMG)
Module 1 - 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
Module 2 - Model Garden
- Introduction to Model Garden
- Model types in Model Garden
- Connecting models from Gen AI Studio and Model Registry
- Introduction to course use cases
Module 3 - 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
Module 4 - 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
Module 5 - Fine-tunable Models
- Fine-tunable models in Model Garden
- Vertex AI Pipelines
- Demo: Fine-tuning models for your specific use case