Multimodal LLM Workflows in Vertex AI Training Course

Fundamentals of Multimodal LLMs in Vertex AI

• Exploring the multimodal features available in Vertex AI
• Review of Gemini models and their compatible data modalities
• Application scenarios in enterprise settings and academic research

Preparation of the Development Environment

• Setting up Vertex AI specifically for multimodal workflows
• Managing datasets that span multiple data modalities
• Practical lab: configuring the environment and preparing datasets

Long Context Windows and Sophisticated Reasoning

• Concepts underlying long-context processing workflows
• Applications in strategic planning and decision support
• Practical lab: executing long-context analysis tasks

Architecting Cross-Modal Workflows

• Synthesizing analysis from text, audio, and image sources
• Sequencing multimodal stages within a unified pipeline
• Practical lab: structuring a comprehensive multimodal pipeline

Managing Gemini API Parameters

• Defining configurations for multimodal data exchange
• Strategies for improving inference speed and operational efficiency
• Practical lab: fine-tuning Gemini API settings

Advanced Implementations and System Integrations

• Developing interactive multimodal agents and assistants
• Connecting with external APIs and third-party tools
• Practical lab: constructing a complete multimodal application

Assessment and Continuous Improvement

• Conducting tests on multimodal system performance
• Defining metrics for accuracy, alignment, and data drift
• Practical lab: evaluating the effectiveness of multimodal workflows

Conclusion and Future Directions