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Duration 14 hours
Course Outline
Introduction to Multimodal LLMs on Vertex AI
- Overview of multimodal features within Vertex AI
- Introduction to Gemini models and their supported modalities
- Enterprise and research application scenarios
Preparing the Development Environment
- Configuring Vertex AI for multimodal workflow requirements
- Managing datasets across various modalities
- Practical lab: Setting up the environment and preparing datasets
Long-Context Windows and Complex Reasoning
- Exploring the mechanics of long-context workflows
- Applications in strategic planning and decision-making
- Practical lab: Implementing long-context analysis tasks
Designing Cross-Modal Workflows
- Synthesizing text, audio, and image analytical processes
- Orchestrating multimodal steps within a pipeline
- Practical lab: Constructing a functional multimodal pipeline
Managing Gemini API Parameters
- Setting up multimodal input and output configurations
- Enhancing inference speed and operational efficiency
- Practical lab: Adjusting and optimizing Gemini API parameters
Sophisticated Applications and System Integrations
- Creating interactive multimodal agents and virtual assistants
- Connecting external APIs and third-party tools
- Practical lab: Developing a comprehensive multimodal application
Assessment and Iterative Improvement
- Conducting performance tests for multimodal systems
- Evaluating accuracy, alignment, and model drift metrics
- Practical lab: Analyzing and evaluating multimodal workflow performance
Recap and Future Directions
Requirements
- Strong proficiency in Python programming
- Practical experience in developing machine learning models
- Understanding of multimodal data types (text, audio, and image)
Target Audience
- AI researchers
- Advanced developers
- Machine Learning scientists
