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Duration 14 hours
Course Outline
Fundamentals of Multimodal LLMs on Vertex AI
- Overview of multimodal features available in Vertex AI
- Exploring Gemini models and their supported data modalities
- Applications in enterprise settings and research domains
Establishing the Development Environment
- Configuring Vertex AI to support multimodal workflows
- Managing datasets that span multiple modalities
- Practical lab: setting up the environment and preparing datasets
Leveraging Long Context Windows for Advanced Reasoning
- Concepts behind long-context workflow architecture
- Applications in strategic planning and decision support
- Practical lab: executing long-context analysis tasks
Architecting Cross-Modal Workflows
- Synthesizing text, audio, and image data streams
- Orchestrating multimodal steps within integrated pipelines
- Practical lab: constructing a complete multimodal pipeline
Optimizing Gemini API Parameters
- Configuring inputs and outputs for multimodal interactions
- Enhancing inference speed and operational efficiency
- Practical lab: fine-tuning Gemini API settings
Sophisticated Applications and System Integrations
- Creating interactive multimodal agents and intelligent assistants
- Connecting with external APIs and auxiliary tools
- Practical lab: developing a full-scale multimodal application
Evaluation Strategies and Iterative Improvement
- Methods for assessing multimodal performance
- Defining metrics for accuracy, alignment, and model drift
- Practical lab: evaluating and refining multimodal workflows
Conclusion and Future Directions
Requirements
- Strong proficiency in Python programming
- Practical experience in developing machine learning models
- Working knowledge of multimodal data types, including text, audio, and images
Target Audience
- AI researchers
- Senior-level developers
- Machine Learning scientists
