Multimodal LLM Workflows in Vertex AI Training Course

Foundations of Multimodal LLMs in Vertex AI

• An overview of multimodal capabilities within the Vertex AI ecosystem
• Exploration of Gemini models and their supported data modalities
• Applications in enterprise operations and academic research

Establishing the Development Environment

• Configuring Vertex AI specifically for multimodal operations
• Managing datasets that span various modalities
• Practical lab: Setting up the environment and preparing datasets

Long Context Windows and Enhanced Reasoning

• Grasping the mechanics of long-context workflows
• Applications in strategic planning and decision-making processes
• Practical lab: Executing long-context analysis tasks

Architecting Cross-Modal Workflows

• Synthesizing text, audio, and image analysis components
• Connecting multimodal steps within continuous pipelines
• Practical lab: Designing a comprehensive multimodal pipeline

Managing Gemini API Parameters

• Setting up multimodal inputs and configuring outputs
• Enhancing inference speed and operational efficiency
• Practical lab: Adjusting Gemini API parameters for optimal results

Advanced Applications and System Integrations

• Creating interactive multimodal agents and intelligent assistants
• Incorporating external APIs and third-party tools
• Practical lab: Developing a full-scale multimodal application

Assessment and Iterative Improvement

• Conducting tests on multimodal performance
• Applying metrics for accuracy, alignment, and drift detection
• Practical lab: Evaluating the effectiveness of multimodal workflows

Conclusion and Future Directions