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Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Exploring the multimodal capabilities offered by Vertex AI
- An overview of Gemini models and their supported modalities
- Examining enterprise and research use cases
Establishing the Development Environment
- Configuring Vertex AI to support multimodal workflows
- Managing datasets across different modalities
- Hands-on lab: Setting up the environment and preparing datasets
Long Context Windows and Advanced Reasoning
- Gaining insight into long-context workflow mechanics
- Applying concepts to planning and decision-making scenarios
- Hands-on lab: Executing long-context analysis
Cross-Modal Workflow Design
- Synthesizing text, audio, and image analytics
- Sequencing multimodal steps within pipeline architectures
- Hands-on lab: Architecting a multimodal pipeline
Managing Gemini API Parameters
- Setting up multimodal inputs and outputs
- Enhancing inference speed and operational efficiency
- Hands-on lab: Fine-tuning Gemini API parameters
Advanced Applications and Integrations
- Developing interactive multimodal agents and assistants
- Connecting external APIs and third-party tools
- Hands-on lab: Developing a comprehensive multimodal application
Evaluation and Iterative Improvement
- Assessing multimodal system performance
- Utilizing metrics for accuracy, alignment, and drift detection
- Hands-on lab: Evaluating the effectiveness of multimodal workflows
Conclusion and Recommended Next Steps
Requirements
- Solid proficiency in Python programming
- Practical experience in developing machine learning models
- Working knowledge of multimodal data types, including text, audio, and images
Target Audience
- AI researchers
- Senior developers
- Machine learning scientists
14 Hours
