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Duration 14 hours
Course Outline
Foundations of Multimodal LLMs in Vertex AI
- Overview of multimodal features available in Vertex AI
- Insight into Gemini models and their supported data modalities
- Real-world applications in enterprise and research settings
Establishing the Development Environment
- Configuring Vertex AI settings for multimodal operations
- Managing datasets that span multiple data types
- Practical exercise: Initializing the environment and preparing datasets
Extended Context Windows and Advanced Reasoning
- Exploring the mechanics of long-context workflows
- Applications in strategic planning and complex decision-making
- Practical exercise: Executing long-context analysis tasks
Architecting Cross-Modal Workflows
- Synthesizing text, audio, and image analysis
- Orchestrating multimodal stages within data pipelines
- Practical exercise: Constructing a comprehensive multimodal pipeline
Optimizing Gemini API Configurations
- Setting up multimodal input and output structures
- Enhancing inference speed and operational efficiency
- Practical exercise: Adjusting Gemini API parameters for optimal results
Advanced Implementations and Integrations
- Developing interactive multimodal agents and virtual assistants
- Incorporating third-party APIs and external tools
- Practical exercise: Building a complete multimodal application
Performance Evaluation and Refinement
- Conducting tests on multimodal model performance
- Applying metrics for accuracy, alignment, and drift detection
- Practical exercise: Assessing multimodal workflow efficacy
Recap and Future Directions
Requirements
- Strong command of Python programming
- Prior experience in developing machine learning models
- Working knowledge of multimodal data types, including text, audio, and images
Target Audience
- AI researchers
- Senior-level developers
- Machine learning scientists