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Vertex AI Model Garden

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....

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$900 USD
Course Code GCP-AIMG
Duration 1 day
Available Formats Classroom, Virtual

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.

Skills Gained

  • 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.

Prerequisites

  • 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.

Course Details

Course Outline

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

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

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

  • Fine-tunable models in Model Garden
  • Vertex AI Pipelines
  • Demo: Fine-tuning models for your specific use case
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