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Duration 14 hours
Course Outline
Fundamentals of Multimodal LLMs on Vertex AI
- Exploring multimodal capabilities within Vertex AI
- An overview of Gemini models and their supported modalities
- Key use cases across enterprise and research sectors
Preparing the Development Environment
- Configuring Vertex AI for multimodal workflow deployment
- Managing datasets that span multiple modalities
- Lab session: Setting up the environment and preparing datasets
Mastering Long Context Windows and Advanced Reasoning
- Comprehending the mechanics of long-context workflows
- Applications in strategic planning and decision-making processes
- Lab session: Implementing long-context analysis techniques
Designing Cross-Modal Workflows
- Synthesizing text, audio, and image analysis capabilities
- Orchestrating multimodal steps within integrated pipelines
- Lab session: Architecting a comprehensive multimodal pipeline
Optimizing Gemini API Parameters
- Setting up multimodal inputs and outputs effectively
- Enhancing inference speed and operational efficiency
- Lab session: Fine-tuning Gemini API parameters
Advanced Applications and System Integrations
- Developing interactive multimodal agents and assistants
- Connecting external APIs and third-party tools
- Lab session: Constructing a functional multimodal application
Assessment and Continuous Improvement
- Conducting performance tests on multimodal systems
- Evaluating metrics related to accuracy, alignment, and model drift
- Lab session: Assessing the efficacy of multimodal workflows
Wrap-up and Future Directions
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
- Advanced software developers
- Machine learning scientists
