Vertex AI Model Garden
Request Info|
01 Jul 2024 - 01 Jul 2024 | Vertex AI Model Garden |
Description
Vertex AI Model Garden provides enterprise-ready foundation models, task-speciuc 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.
Audience and prerequisites
Machine learning practitioners who wish to leverage models available in Vertex AI Model Garden for various use cases.
Prerequisites:
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.
Objectives
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.
Topics
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-speciuc Solutions: Content Classification
- Pre-trained models for specific tasks.
- VertexAI AutoML.
- Using a pre-trained model via the Python SDK.
- Lab: Content Classiucation 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.
Open calls
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01 Jul 2024 - 01 Jul 2024 | Vertex AI Model Garden |
