Multimodal LLM Workflows in Vertex AI Training Course

Foundations of Multimodal LLMs in Vertex AI

• Exploring the scope of multimodal features in Vertex AI
• Review of Gemini models and compatible data modalities
• Applications in enterprise environments and research settings

Establishing the Development Environment

• Configuring Vertex AI specifically for multimodal operations
• Managing datasets across various data types
• Practical exercise: Setting up the environment and preparing datasets

Long Context Windows and Sophisticated Reasoning

• Comprehending the mechanics of long-context workflows
• Applying these concepts to planning and decision-making processes
• Practical exercise: Executing long-context analysis

Architecting Cross-Modal Workflows

• Synthesizing text, audio, and image analytical capabilities
• Orchestrating sequential multimodal steps within pipelines
• Practical exercise: Constructing a comprehensive multimodal pipeline

Managing Gemini API Parameters

• Setting up configurations for multimodal input and output streams
• Enhancing inference speed and operational efficiency
• Practical exercise: Fine-tuning Gemini API settings

Advanced Implementations and System Integrations

• Developing interactive multimodal agents and digital assistants
• Connecting external APIs and third-party tools
• Practical exercise: Building a functional multimodal application

Assessment and Continuous Improvement

• Conducting performance tests on multimodal models
• Applying metrics for accuracy, alignment, and drift detection
• Practical exercise: Evaluating the efficacy of multimodal workflows

Recap and Future Directions