Multimodal LLM Workflows in Vertex AI Training Course

Introduction to Multimodal LLMs in Vertex AI

• Overview of multimodal capabilities within Vertex AI.
• Deep dive into Gemini models and their supported modalities.
• Exploration of relevant use cases in enterprise and research settings.

Setting Up the Development Environment

• Configuring Vertex AI specifically for multimodal workflows.
• Managing datasets across various modalities.
• Hands-on lab: Environment setup and dataset preparation.

Long Context Windows and Advanced Reasoning

• Understanding the mechanics of long-context workflows.
• Practical applications in planning and decision-making processes.
• Hands-on lab: Implementing long-context analysis.

Cross-Modal Workflow Design

• Integrating text, audio, and image analysis into cohesive solutions.
• Chaining multimodal steps within streamlined pipelines.
• Hands-on lab: Designing a comprehensive multimodal pipeline.

Working with Gemini API Parameters

• Configuring inputs and outputs for multimodal scenarios.
• Strategies for optimizing inference speed and resource efficiency.
• Hands-on lab: Tuning Gemini API parameters.

Advanced Applications and Integrations

• Developing interactive multimodal agents and virtual assistants.
• Seamless integration of external APIs and supplementary tools.
• Hands-on lab: Building a functional multimodal application.

Evaluation and Iteration

• Methods for testing multimodal performance.
• Key metrics for assessing accuracy, alignment, and data drift.
• Hands-on lab: Evaluating multimodal workflows.

Summary and Next Steps