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Duration 14 hours
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal capabilities available in Vertex AI
- Introduction to Gemini models and their supported modalities
- Key use cases in enterprise and research settings
Configuring the Development Environment
- Setting up Vertex AI for multimodal workflows
- Managing datasets across different modalities
- Hands-on lab: environment configuration and dataset preparation
Long Context Windows and Advanced Reasoning
- Understanding the mechanics of long-context workflows
- Applications in planning and decision-making processes
- Hands-on lab: implementing long-context analysis
Cross-Modal Workflow Design
- Integrating text, audio, and image analytics
- Chaining multimodal steps within pipelines
- Hands-on lab: designing a comprehensive multimodal pipeline
Working with Gemini API Parameters
- Configuring multimodal inputs and outputs effectively
- Optimising inference speed and overall efficiency
- Hands-on lab: tuning Gemini API parameters
Advanced Applications and Integrations
- Developing interactive multimodal agents and assistants
- Integrating external APIs and third-party tools
- Hands-on lab: building a complete multimodal application
Evaluation and Iteration
- Testing multimodal performance effectively
- Using metrics for accuracy, alignment, and drift detection
- Hands-on lab: evaluating multimodal workflows
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- Hands-on experience with machine learning model development
- Working knowledge of multimodal data types (text, audio, image)
Target Audience
- AI researchers
- Senior developers
- ML engineers and scientists
