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Duration 14 hours
Course Outline
Foundations of Multimodal LLMs in Vertex AI
- Examining the multimodal capabilities available in Vertex AI
- Exploring Gemini models and their supported modalities
- Identifying key use cases in enterprise and research settings
Preparing the Development Environment
- Configuring Vertex AI for multimodal workflow execution
- Managing datasets spanning multiple data modalities
- Practical lab: setting up the environment and preparing datasets
Long Context Windows and Complex Reasoning
- Analyzing the structure and utility of long-context workflows
- Applying these capabilities to planning and decision-making processes
- Practical lab: executing long-context data analysis
Designing Cross-Modal Workflows
- Synthesizing text, audio, and image analysis into unified processes
- Orchestrating multimodal steps within structured pipelines
- Practical lab: constructing a comprehensive multimodal pipeline
Managing Gemini API Parameters
- Configuring inputs and outputs for multimodal tasks
- Enhancing inference speed and overall efficiency
- Practical lab: fine-tuning Gemini API settings
Sophisticated Applications and System Integrations
- Creating interactive multimodal agents and intelligent assistants
- Connecting external APIs and third-party tools
- Practical lab: developing a fully functional multimodal application
Assessment and Continuous Improvement
- Conducting rigorous performance testing of multimodal systems
- Applying metrics for accuracy, alignment, and model drift
- Practical lab: evaluating the effectiveness of multimodal workflows
Concluding Summary and Future Directions
Requirements
- Solid command of Python programming
- Prior experience in developing machine learning models
- Working knowledge of multimodal data types (text, audio, image)
Intended Audience
- AI researchers
- Senior developers
- ML scientists
