Multimodal LLM Workflows in Vertex AI Training Course

Introduction to Multimodal LLMs in Vertex AI

• Exploring multimodal capabilities within the Vertex AI ecosystem
• Overview of Gemini models and their supported data modalities
• Application scenarios in enterprise environments and research settings

Establishing the Development Environment

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

Long Context Windows and Advanced Reasoning

• Concepts and mechanics of long-context workflows
• Applying these capabilities to planning and decision-making processes
• Practical lab: Executing long-context data analysis

Architecting Cross-Modal Workflows

• Merging text, audio, and image analysis techniques
• Orchestrating multimodal steps within automated pipelines
• Practical lab: Designing an end-to-end multimodal pipeline

Managing Gemini API Parameters

• Setting up inputs and outputs for multimodal interactions
• Enhancing inference speed and operational efficiency
• Practical lab: Tuning specific Gemini API parameters

Advanced Applications and System Integrations

• Creating interactive multimodal agents and intelligent assistants
• Connecting with external APIs and third-party tools
• Practical lab: Developing a functional multimodal application

Evaluation and Continuous Improvement

• Testing methods for multimodal performance
• Key metrics for assessing accuracy, alignment, and model drift
• Practical lab: Conducting comprehensive workflow evaluations

Conclusion and Future Pathways