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Course Outline
Introduction to Multimodal LLMs on Vertex AI
- Overview of multimodal features available in Vertex AI
- Introduction to Gemini models and their supported modalities
- Enterprise and research use cases
Preparing the Development Environment
- Configuring Vertex AI for multimodal processing
- Handling datasets across different modalities
- Practical lab: Environment setup and data preparation
Long Context Windows and Complex Reasoning
- Concepts of long-context workflow management
- Applications in planning and decision-making processes
- Practical lab: Executing long-context analysis
Designing Cross-Modal Workflows
- Integration of text, audio, and image analysis components
- Sequencing multimodal steps within pipelines
- Practical lab: Architecting a multimodal pipeline
Managing Gemini API Parameters
- Setup for multimodal input and output configurations
- Strategies for optimizing inference speed and efficiency
- Practical lab: Adjusting Gemini API settings
Advanced Applications and Integrations
- Creating interactive multimodal agents and assistants
- Connecting external APIs and tools
- Practical lab: Developing a comprehensive multimodal application
Assessment and Refinement
- Methods for testing multimodal system performance
- Key metrics for accuracy, alignment, and data drift
- Practical lab: Evaluating multimodal workflow effectiveness
Conclusion and Future Directions
Requirements
- Strong proficiency in Python programming
- Practical experience in machine learning model development
- Working knowledge of multimodal data types, including text, audio, and images
Target Audience
- AI researchers
- Advanced developers
- Machine Learning scientists
14 Hours
