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Duration 14 hours
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Exploration of multimodal capabilities within Vertex AI
- Overview of Gemini models and their supported modalities
- Application scenarios in enterprise and research contexts
Establishing the Development Environment
- Configuring Vertex AI for multimodal workflow execution
- Managing datasets that span multiple modalities
- Hands-on lab: environment setup and dataset preparation
Long Context Windows and Advanced Reasoning
- In-depth analysis of long-context workflows
- Applications in strategic planning and decision-making processes
- Hands-on lab: implementing long-context analysis techniques
Cross-Modal Workflow Design
- Synthesizing text, audio, and image analysis
- Orchestrating multimodal steps within pipelines
- Hands-on lab: designing a comprehensive multimodal pipeline
Working with Gemini API Parameters
- Configuration of multimodal inputs and outputs
- Strategies for optimizing inference speed and efficiency
- Hands-on lab: tuning Gemini API parameters for optimal results
Advanced Applications and Integrations
- Development of interactive multimodal agents and assistants
- Integration of external APIs and third-party tools
- Hands-on lab: building a functional multimodal application
Evaluation and Iteration
- Methods for testing multimodal performance
- Key metrics for assessing accuracy, alignment, and model drift
- Hands-on lab: evaluating the effectiveness of multimodal workflows
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- Practical experience in machine learning model development
- Working knowledge of multimodal data types (text, audio, and image)
Target Audience
- AI researchers
- Advanced developers
- ML scientists
