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Duration 14 hours
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Exploring the scope of multimodal capabilities in Vertex AI
- Overview of Gemini models and their supported modalities
- Key use cases across enterprise and research sectors
Setting Up the Development Environment
- Configuring Vertex AI to support multimodal workflows
- Managing datasets spanning different modalities
- Hands-on lab: environment setup and dataset preparation
Long Context Windows and Advanced Reasoning
- Understanding the mechanics of long-context workflows
- Applications in strategic planning and decision-making
- Hands-on lab: implementing long-context analysis
Cross-Modal Workflow Design
- Synthesizing text, audio, and image analysis
- Chaining multimodal steps within pipelines
- Hands-on lab: designing a multimodal pipeline
Working with Gemini API Parameters
- Configuring multimodal inputs and outputs
- Optimizing inference speed and efficiency
- Hands-on lab: tuning Gemini API parameters
Advanced Applications and Integrations
- Developing interactive multimodal agents and assistants
- Integrating external APIs and tools
- Hands-on lab: building a comprehensive multimodal application
Evaluation and Iteration
- Testing multimodal performance benchmarks
- Utilizing metrics for accuracy, alignment, and drift
- Hands-on lab: evaluating multimodal workflows
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- Practical experience in machine learning model development
- Working knowledge of multimodal data types, including text, audio, and images
Target Audience
- AI researchers
- Advanced developers
- ML scientists
