Multimodal LLM Workflows in Vertex AI Training Course

Introduction to Multimodal LLMs in Vertex AI

• Explore the multimodal capabilities available in Vertex AI
• Review Gemini models and their supported modalities
• Examine enterprise and research use cases

Setting Up the Development Environment

• Configure Vertex AI specifically for multimodal workflows
• Manage datasets across different modalities
• Hands-on lab: Setting up the environment and preparing datasets

Long Context Windows and Advanced Reasoning

• Gain insight into long-context workflow mechanics
• Apply concepts to planning and decision-making scenarios
• Hands-on lab: Implementing long-context analysis

Cross-Modal Workflow Design

• Synthesize text, audio, and image analysis techniques
• Chain multimodal steps effectively within pipelines
• Hands-on lab: Designing a comprehensive multimodal pipeline

Working with Gemini API Parameters

• Configure multimodal inputs and outputs
• Optimize inference speed and operational efficiency
• Hands-on lab: Tuning Gemini API parameters

Advanced Applications and Integrations

• Create interactive multimodal agents and assistants
• Integrate external APIs and tools into the workflow
• Hands-on lab: Building a functional multimodal application

Evaluation and Iteration

• Test the performance of multimodal models
• Apply metrics for accuracy, alignment, and drift detection
• Hands-on lab: Evaluating multimodal workflows

Summary and Next Steps