Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Multimodal LLMs within Vertex AI
- Exploring the multimodal capabilities of Vertex AI
- Overview of Gemini models and their supported modalities
- Application scenarios in enterprise and research settings
Establishing the Development Environment
- Configuring Vertex AI for multimodal workflows
- Managing datasets across different modalities
- Hands-on lab: setting up the environment and preparing datasets
Long Context Windows and Enhanced Reasoning
- Grasping the mechanics of long-context workflows
- Applications in strategic planning and decision-making
- Hands-on lab: executing long-context analysis
Architecting Cross-Modal Workflows
- Integrating text, audio, and image analysis
- Sequencing multimodal steps within pipelines
- Hands-on lab: constructing a multimodal pipeline
Managing Gemini API Parameters
- Setting up multimodal inputs and outputs
- Refining inference processes for efficiency
- Hands-on lab: adjusting Gemini API parameters
Advanced Applications and System Integrations
- Creating interactive multimodal agents and assistants
- Incorporating external APIs and tools
- Hands-on lab: developing a multimodal application
Evaluation and Iterative Improvement
- Assessing multimodal performance
- Applying metrics for accuracy, alignment, and drift
- Hands-on lab: evaluating multimodal workflow efficacy
Conclusion and Future Directions
Requirements
- Strong proficiency in Python programming
- Practical experience in developing machine learning models
- Working knowledge of multimodal data types (text, audio, image)
Target Audience
- AI researchers
- Senior developers
- ML engineers
