Multimodal LLM Workflows in Vertex AI Training Course

Introduction to Multimodal LLMs in Vertex AI

• An overview of multimodal capabilities offered by Vertex AI
• Introduction to Gemini models and their supported modalities
• Exploration of enterprise and research use cases

Setting Up the Development Environment

• Configuration of Vertex AI for multimodal workflow development
• Managing datasets that span multiple modalities
• Practical lab: Environment setup and dataset preparation

Long Context Windows and Advanced Reasoning

• Concepts behind long-context workflows
• Applications in strategic planning and decision-making processes
• Practical lab: Implementing long-context analysis techniques

Cross-Modal Workflow Design

• Integrating analysis from text, audio, and image sources
• Chaining multiple multimodal steps within a single pipeline
• Practical lab: Designing and assembling a multimodal pipeline

Working with Gemini API Parameters

• Configuring parameters for multimodal inputs and outputs
• Strategies for optimizing inference speed and operational efficiency
• Practical lab: Tuning Gemini API parameters for optimal results

Advanced Applications and Integrations

• Development of interactive multimodal agents and assistants
• Methods for integrating external APIs and third-party tools
• Practical lab: Building a comprehensive multimodal application

Evaluation and Iteration

• Techniques for testing the performance of multimodal systems
• Key metrics for assessing accuracy, alignment, and model drift
• Practical lab: Evaluating and refining multimodal workflows

Summary and Next Steps