Multimodal LLM Workflows in Vertex AI Training Course
Vertex AI offers robust tools for constructing multimodal LLM workflows that seamlessly integrate text, audio, and image data into a unified pipeline. Featuring support for long context windows and Gemini API parameters, it facilitates advanced applications in planning, reasoning, and cross-modal intelligence.
This instructor-led live training, available online or on-site, is designed for intermediate to advanced practitioners looking to design, build, and optimize multimodal AI workflows within Vertex AI.
Upon completion of this training, participants will be able to:
- Utilize Gemini models for handling multimodal inputs and outputs.
- Develop long-context workflows to tackle complex reasoning tasks.
- Create pipelines that combine text, audio, and image analysis.
- Optimize Gemini API parameters to enhance performance and cost efficiency.
Course Format
- Interactive lectures and discussions.
- Practical labs focused on multimodal workflows.
- Project-based exercises applying multimodal use cases.
Customization Options
- For customized training requests, please contact us to arrange your session.
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal capabilities in Vertex AI
- Gemini models and supported modalities
- Use cases in enterprise and research
Setting Up the Development Environment
- Configuring Vertex AI for multimodal workflows
- Working with datasets across modalities
- Hands-on lab: environment setup and dataset preparation
Long Context Windows and Advanced Reasoning
- Understanding long-context workflows
- Use cases in planning and decision-making
- Hands-on lab: implementing long-context analysis
Cross-Modal Workflow Design
- Combining text, audio, and image analysis
- Chaining multimodal steps in pipelines
- Hands-on lab: designing a multimodal pipeline
Working with Gemini API Parameters
- Configuring multimodal inputs and outputs
- Optimizing inference and efficiency
- Hands-on lab: tuning Gemini API parameters
Advanced Applications and Integrations
- Interactive multimodal agents and assistants
- Integrating external APIs and tools
- Hands-on lab: building a multimodal application
Evaluation and Iteration
- Testing multimodal performance
- Metrics for accuracy, alignment, and drift
- Hands-on lab: evaluating multimodal workflows
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Experience with machine learning model development
- Familiarity with multimodal data (text, audio, image)
Audience
- AI researchers
- Advanced developers
- ML scientists
Open Training Courses require 5+ participants.
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