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Vertex AI Model Garden

  • Length 1 day
  • Price  NZD 850 exc GST
Course overview
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Why study this course

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 pre-trained models as part of your machine learning workflow and how to fine-tune models for your specific applications.

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What you’ll learn

This course teaches participants the following skills:

  • 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

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Who is the course for?

This course is intended for the following participants:

  • Machine learning practitioners who wish to leverage models available in Vertex AI Model Garden for various different use cases.

Course subjects

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


To get the most out of this course, participants should have:

  • 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.

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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