1 day course

Supporting material

Private

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

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. On this course, after being introduced to Vertex AI as a machine learning platform through the lens of Model Garden, you will learn how to leverage pre-trained models as part of your machine learning workflow and how to fine-tune models for your specific applications.

This Vertex AI Model Garden course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in the US.

Course overview

Who should attend:

This course is ideal for machine learning practitioners who want to leverage models available in Vertex AI Model Garden for various different use cases.

What you'll learn:

By the end of this course, you will be able to:

Understand the model options available within Vertex AI Model Garden
Incorporate models in Vertex AI Model Garden into your machine learning workflows
Leverage foundation models for generative AI use cases
Fine-tune models to meet your specific needs

Prerequisites

To get the most out of this course, participants should have completed the Machine Learning on Google Cloud course or have the equivalent knowledge of TensorFlow / Keras and machine learning. They should also have some experience scripting in Python and working in Jupyter notebooks to create machine learning models.

Course agenda

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 AutoM
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