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Duration 14 hours
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal features within Vertex AI
- Gemini models and their supported modalities
- Applications in enterprise and research settings
Preparing the Development Environment
- Configuring Vertex AI for multimodal workflows
- Managing datasets across various modalities
- Hands-on lab: setting up the environment and preparing datasets
Long Context Windows and Advanced Reasoning
- Exploring the mechanics of long-context workflows
- Applications in planning and decision-making processes
- Hands-on lab: executing long-context analysis
Cross-Modal Workflow Design
- Synthesizing text, audio, and image analysis
- Linking multimodal steps within pipelines
- Hands-on lab: constructing a multimodal pipeline
Managing Gemini API Parameters
- Setting up multimodal inputs and outputs
- Enhancing inference speed and overall efficiency
- Hands-on lab: adjusting Gemini API parameters
Advanced Applications and Integrations
- Creating interactive multimodal agents and assistants
- Connecting external APIs and tools
- Hands-on lab: developing a comprehensive multimodal application
Evaluation and Iteration
- Assessing multimodal performance
- Defining metrics for accuracy, alignment, and drift
- Hands-on lab: evaluating multimodal workflows
Conclusion and Future Steps
Requirements
- Strong proficiency in Python programming
- Hands-on experience in machine learning model development
- Familiarity with handling multimodal data such as text, audio, and images
Target Audience
- AI researchers
- Senior developers
- ML scientists
