Multimodal LLM Workflows in Vertex AI Training Course

Introduction to Multimodal LLMs within Vertex AI

• Exploring multimodal capabilities inherent to Vertex AI
• Examining Gemini models and their supported modalities
• Identifying relevant use cases for enterprise and research sectors

Establishing the Development Environment

• Configuring Vertex AI for multimodal workflow operations
• Managing datasets across various data modalities
• Practical lab: Setting up the environment and preparing datasets

Long Context Windows and Advanced Reasoning

• Comprehending the mechanics of long-context workflows
• Applying these workflows to planning and decision-making processes
• Practical lab: Implementing and analyzing long-context scenarios

Cross-Modal Workflow Architecture

• Synthesizing analysis from text, audio, and image sources
• Orchestrating multimodal steps within integrated pipelines
• Practical lab: Designing a comprehensive multimodal pipeline

Optimizing Gemini API Parameters

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

Advanced Applications and System Integrations

• Developing interactive multimodal agents and assistants
• Connecting external APIs and third-party tools
• Practical lab: Constructing a complete multimodal application

Evaluation Strategies and Iterative Improvement

• Conducting performance tests for multimodal systems
• Utilizing metrics for accuracy, alignment, and drift detection
• Practical lab: Evaluating and refining multimodal workflows

Summary and Recommended Next Steps