GCP-AIMG

Vertex AI Model Garden Training

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.
Course Details

Duration

1 day

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.

Target Audience

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

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
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
  • Foundation Models: Text Embeddings via PaLM
    • Introduction to foundation models
    • PaLM API
    • GenAI Studio
    • Using the Embeddings API
  • Fine-tunable Models
    • Fine-tunable models in Model Garden
    • Vertex AI Pipelines