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.
Este curso enseña a los participantes las siguientes habilidades:
- 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
Esta clase está dirigida a la siguiente audiencia:
- Machine learning practitioners who wish to leverage models available in Vertex AI Model Garden for various different use cases.
Basic understanding of one or more of the following:
- 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.
Resumen del curso
1 day of introductory to intermediate-level training for machine learning practitioners to get started with Vertex AI Model Garden This class includes lecture, demonstrations and hands-on lab activities.
- 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
- Introduction to Model Garden
- Model types in Model Garden
- Connecting models from Gen AI Studio and Model Registry
- Introduction to course use cases
- 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
- 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 in Model Garden
- Vertex AI Pipelines
- Demo: Fine-tuning models for your specific use case