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Duration 14 hours
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- An overview of Vertex AI’s multimodal features
- Gemini models and the modalities they support
- Applications in enterprise and research settings
Preparing the Development Environment
- Configuring Vertex AI for multimodal operations
- Managing datasets across different modalities
- Practical lab: Setting up the environment and preparing datasets
Long Context Windows and Advanced Reasoning
- Concepts behind long-context workflows
- Applications in strategic planning and decision-making
- Practical lab: Building long-context analysis solutions
Designing Cross-Modal Workflows
- Synthesizing text, audio, and image data
- Linking multimodal steps within a pipeline
- Practical lab: Constructing a multimodal pipeline
Managing Gemini API Parameters
- Setting up multimodal inputs and outputs
- Enhancing inference speed and efficiency
- Practical lab: Fine-tuning Gemini API settings
Advanced Applications and Integrations
- Creating interactive multimodal agents and assistants
- Connecting external APIs and tools
- Practical lab: Developing a comprehensive multimodal application
Evaluation and Iterative Improvement
- Assessing multimodal performance
- Key metrics for accuracy, alignment, and model drift
- Practical lab: Evaluating multimodal workflow effectiveness
Wrap-up and Future Directions
Requirements
- Strong proficiency in Python
- Practical experience in developing machine learning models
- Knowledge of handling multimodal data types, including text, audio, and images
Target Audience
- AI Researchers
- Senior Developers
- Machine Learning Scientists
