1 day course 
Supporting material 
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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. On this course, 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.
This Vertex AI Model Garden course is available as a private training session that can be delivered via Virtual Classroom, at our training centre in The Shard, or at a location of your choice in the UK.
Course overview
Who should attend:
This course is ideal for machine learning practitioners who want to leverage models available in Vertex AI Model Garden for various different use cases.
What you'll learn:
By the end of this course, you will be able to:
- Understand the model options available within Vertex AI Model Garden
- Incorporate models in Vertex AI Model Garden into your machine learning workflows
- Leverage foundation models for generative AI use cases
- Fine-tune models to meet your specific needs
Prerequisites
To get the most out of this course, participants should have completed the Machine Learning on Google Cloud course or have the equivalent knowledge of TensorFlow / Keras and machine learning. They should also have some experience scripting in Python and working in Jupyter notebooks to create machine learning models.
Course agenda
- 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 AutoM
- 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