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Duration 14 hours
Course Outline
Foundations of Multimodal LLMs in Vertex AI
- A comprehensive look at Vertex AI’s multimodal capabilities
- An introduction to Gemini models and their supported data modalities
- Exploring practical applications in enterprise and research settings
Establishing the Development Environment
- Configuring Vertex AI specifically for multimodal pipeline management
- Handling datasets that span different data modalities
- Practical lab: Setting up the environment and preparing datasets
Long Context Windows and Enhanced Reasoning
- Concepts behind long-context workflow implementation
- Applying these workflows to planning and decision-making scenarios
- Practical lab: Executing long-context data analysis
Architecting Cross-Modal Workflows
- Synthesizing text, audio, and image analysis techniques
- Orchestrating multimodal steps within a unified pipeline
- Practical lab: Designing and testing a multimodal pipeline
Mastering Gemini API Parameters
- Setting up multimodal input and output configurations
- Strategies for optimizing inference speed and operational efficiency
- Practical lab: Tuning Gemini API parameters for best results
Sophisticated Applications and System Integrations
- Creating interactive multimodal agents and intelligent assistants
- Connecting with external APIs and third-party tools
- Practical lab: Developing a complete multimodal application
Assessment and Continuous Improvement
- Methods for testing the performance of multimodal systems
- Evaluating accuracy, alignment, and data drift metrics
- Practical lab: Conducting a comprehensive evaluation of multimodal workflows
Recap and Future Pathways
Requirements
- Solid proficiency in Python programming
- Practical experience in developing machine learning models
- Working knowledge of multimodal data types, including text, audio, and images
Target Audience
- AI Researchers
- Senior and Advanced Developers
- Machine Learning Scientists
