Multimodal LLM Workflows in Vertex AI Training Course

Foundations of Multimodal LLMs on Vertex AI

• An overview of multimodal capabilities available within Vertex AI.
• Exploration of Gemini models and the specific modalities they support.
• Key enterprise and research use cases for multimodal AI.

Preparing the Development Environment

• Configuring Vertex AI specifically for multimodal workflow execution.
• Techniques for managing datasets that span different modalities.
• Practical Lab: Setting up the environment and preparing multimodal datasets.

Long Context Windows and Advanced Reasoning

• Concepts and mechanics behind long-context workflows.
• Applications in strategic planning and complex decision-making processes.
• Practical Lab: Implementing and testing long-context analysis techniques.

Designing Cross-Modal Workflows

• Strategies for combining text, audio, and image analysis in a single flow.
• Methods for chaining multimodal processing steps within pipelines.
• Practical Lab: Designing and building a functional multimodal pipeline.

Optimizing Gemini API Parameters

• Techniques for configuring multimodal inputs and outputs effectively.
• Best practices for optimizing inference speed and overall efficiency.
• Practical Lab: Tuning Gemini API parameters for specific performance goals.

Advanced Applications and System Integrations

• Developing interactive multimodal agents and intelligent assistants.
• Methods for integrating external APIs and third-party tools.
• Practical Lab: Building a complete multimodal application from scratch.

Evaluation Strategies and Iterative Improvement

• Approaches for testing multimodal model performance.
• Key metrics for assessing accuracy, alignment, and data drift.
• Practical Lab: Evaluating and refining multimodal workflow outputs.

Course Summary and Recommended Next Steps