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Duration 14 hours
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Exploring the multimodal capabilities within Vertex AI
- Overview of Gemini models and their supported modalities
- Applications in enterprise and research settings
Preparing the Development Environment
- Setting up Vertex AI for multimodal workflows
- Managing datasets across different modalities
- Practical lab: Configuring the environment and preparing datasets
Long Context Windows and Sophisticated Reasoning
- Concepts behind long-context workflows
- Applications in strategic planning and decision-making
- Practical lab: Executing long-context analysis
Architecting Cross-Modal Workflows
- Synthesizing text, audio, and image analysis
- Orchestrating multimodal steps within pipelines
- Practical lab: Creating a multimodal pipeline
Configuring Gemini API Parameters
- Setting up multimodal inputs and outputs
- Enhancing inference speed and operational efficiency
- Practical lab: Fine-tuning Gemini API parameters
Advanced Applications and System Integration
- Developing interactive multimodal agents and assistants
- Connecting external APIs and tools
- Practical lab: Constructing a complete multimodal application
Evaluation and Continuous Improvement
- Benchmarking multimodal performance
- Defining metrics for accuracy, alignment, and model drift
- Practical lab: Assessing multimodal workflow effectiveness
Conclusion and Future Directions
Requirements
- Strong proficiency in Python programming
- Practical experience in developing machine learning models
- Working knowledge of multimodal data types (text, audio, image)
Target Audience
- AI Researchers
- Senior Developers
- ML Scientists
