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Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal features within Vertex AI
- Introduction to Gemini models and their supported modalities
- Enterprise and research use cases
Setting Up the Development Environment
- Configuring Vertex AI for multimodal processing
- Managing datasets across different modalities
- Hands-on lab: environment configuration and dataset preparation
Long Context Windows and Advanced Reasoning
- Exploring long-context workflow capabilities
- 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 a pipeline
- Hands-on lab: constructing a multimodal pipeline
Working with Gemini API Parameters
- Setting up multimodal inputs and outputs
- Enhancing inference speed and resource efficiency
- Hands-on lab: adjusting Gemini API parameters
Advanced Applications and Integrations
- Creating interactive multimodal agents and assistants
- Incorporating external APIs and tools
- Hands-on lab: developing a comprehensive multimodal application
Evaluation and Iteration
- Assessing multimodal performance
- Key metrics for accuracy, alignment, and drift detection
- Hands-on lab: evaluating multimodal workflow effectiveness
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- Background experience in developing machine learning models
- Working knowledge of multimodal data types, including text, audio, and images
Target Audience
- AI researchers
- Senior developers
- Machine learning scientists
14 Hours