Multimodal LLM Workflows in Vertex AI Training Course

Introduction to Multimodal LLMs in Vertex AI

• Overview of multimodal capabilities available in Vertex AI
• Gemini models and the modalities they support
• Enterprise and research application scenarios

Setting Up the Development Environment

• Configuring Vertex AI specifically for multimodal workflows
• Managing datasets across different modalities
• Hands-on lab: setting up the environment and preparing datasets

Long Context Windows and Advanced Reasoning

• Understanding the mechanics of long-context workflows
• Applications in strategic planning and decision-making
• Hands-on lab: executing long-context analysis

Cross-Modal Workflow Design

• Integrating text, audio, and image analysis
• Chaining multimodal steps within pipelines
• Hands-on lab: architecting a multimodal pipeline

Working with Gemini API Parameters

• Configuring inputs and outputs for multimodal tasks
• Optimizing inference speed and resource efficiency
• Hands-on lab: adjusting Gemini API parameters

Advanced Applications and Integrations

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

Evaluation and Iteration

• Testing the performance of multimodal models
• Key metrics for accuracy, alignment, and drift detection
• Hands-on lab: assessing multimodal workflow effectiveness

Summary and Next Steps