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Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal capabilities in Vertex AI.
- Gemini models and supported modalities.
- Enterprise and research use cases.
Setting Up the Development Environment
- Configuring Vertex AI for multimodal workflows.
- Managing datasets across various modalities.
- Hands-on lab: Environment setup and dataset preparation.
Long Context Windows and Advanced Reasoning
- Understanding long-context workflows.
- Use cases in planning and decision-making.
- Hands-on lab: Implementing long-context analysis.
Cross-Modal Workflow Design
- Integrating text, audio, and image analysis.
- Chaining multimodal steps within pipelines.
- Hands-on lab: Designing a multimodal pipeline.
Working with Gemini API Parameters
- Configuring multimodal inputs and outputs.
- Optimizing inference efficiency.
- Hands-on lab: Tuning Gemini API parameters.
Advanced Applications and Integrations
- Interactive multimodal agents and assistants.
- Integrating external APIs and tools.
- Hands-on lab: Building a multimodal application.
Evaluation and Iteration
- Testing multimodal performance.
- Metrics for accuracy, alignment, and drift.
- Hands-on lab: Evaluating multimodal workflows.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Experience in developing machine learning models.
- Familiarity with multimodal data types (text, audio, images).
Audience
- AI researchers.
- Advanced software developers.
- Machine learning scientists.
14 Hours
