Multimodal LLM Workflows in Vertex AI Training Course

Introduction to Multimodal LLMs in Vertex AI

• Comprehensive overview of multimodal capabilities within Vertex AI.
• Deep dive into Gemini models and their supported modalities.
• Exploration of relevant use cases in enterprise environments and research.

Setting Up the Development Environment

• Configuring Vertex AI to support multimodal workflows.
• Managing and manipulating datasets across different modalities.
• Hands-on lab: Environment configuration and dataset preparation.

Long Context Windows and Advanced Reasoning

• Understanding the mechanics of long-context workflows.
• Analyzing applications in strategic planning and decision-making processes.
• Hands-on lab: Implementing long-context analysis techniques.

Cross-Modal Workflow Design

• Integrating text, audio, and image analysis components.
• Chaining multimodal steps to create cohesive pipelines.
• Hands-on lab: Designing an end-to-end multimodal pipeline.

Working with Gemini API Parameters

• Configuring inputs and outputs for multimodal interactions.
• Strategies for optimizing inference speed and computational efficiency.
• Hands-on lab: Fine-tuning Gemini API parameters.

Advanced Applications and Integrations

• Developing interactive multimodal agents and virtual assistants.
• Integrating external APIs and auxiliary tools.
• Hands-on lab: Constructing a fully functional multimodal application.

Evaluation and Iteration

• Methods for testing and validating multimodal performance.
• Key metrics for accuracy, alignment, and detecting data drift.
• Hands-on lab: Conducting comprehensive evaluations of multimodal workflows.

Summary and Next Steps