When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
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
