Multimodal LLM Workflows in Vertex AI Training Course

Foundations of Multimodal LLMs in Vertex AI

• An overview of Vertex AI’s multimodal capabilities
• Exploring Gemini models and their supported modalities
• Applications in enterprise environments and research settings

Preparing the Development Environment

• Setting up Vertex AI for multimodal operations
• Managing datasets across different modalities
• Practical session: Environment configuration and data preparation

Extended Context Windows and Reasoning

• Concepts behind long-context workflows
• Applications in strategic planning and decision support
• Practical session: Executing long-context analysis

Architecting Cross-Modal Workflows

• Synthesizing text, audio, and image analyses
• Linking multimodal steps within pipelines
• Practical session: Building a comprehensive multimodal pipeline

Managing Gemini API Parameters

• Setting up multimodal input and output configurations
• Enhancing inference speed and operational efficiency
• Practical session: Fine-tuning Gemini API settings

Sophisticated Applications and Integrations

• Developing interactive multimodal agents and assistants
• Incorporating external APIs and tools
• Practical session: Creating a full multimodal application

Assessment and Continuous Improvement

• Conducting performance tests for multimodal systems
• Defining metrics for accuracy, alignment, and model drift
• Practical session: Assessing multimodal workflow effectiveness

Conclusions and Future Pathways