Multimodal LLM Workflows in Vertex AI Training Course

Introduction to Multimodal LLMs in Vertex AI

• Overview of multimodal capabilities within Vertex AI
• Gemini models and their supported data modalities
• Enterprise and research use cases

Establishing the Development Environment

• Configuring Vertex AI for multimodal operations
• Handling datasets across different modalities
• Practical lab: setting up the environment and preparing datasets

Long Context Windows and Advanced Reasoning

• Comprehending long-context workflows
• Applications in planning and decision-making processes
• Practical lab: implementing long-context analysis techniques

Designing Cross-Modal Workflows

• Synthesizing text, audio, and image analysis
• Chaining multimodal steps within automated pipelines
• Practical lab: architecting a multimodal pipeline

Managing Gemini API Parameters

• Configuring multimodal inputs and outputs
• Enhancing inference speed and operational efficiency
• Practical lab: fine-tuning Gemini API parameters

Advanced Applications and Integrations

• Developing interactive multimodal agents and assistants
• Integrating with external APIs and tools
• Practical lab: constructing a complete multimodal application

Evaluation and Iteration

• Assessing multimodal performance metrics
• Tracking accuracy, alignment, and drift indicators
• Practical lab: evaluating the effectiveness of multimodal workflows

Recap and Future Directions