Multimodal LLM Workflows in Vertex AI Training Course
Vertex AI offers robust tools for constructing multimodal LLM workflows, seamlessly integrating text, audio, and image data into a unified pipeline. Leveraging long context window capabilities and Gemini API parameters, it empowers advanced applications in planning, reasoning, and cross-modal intelligence.
This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners aiming to design, build, and optimize multimodal AI workflows within Vertex AI.
Upon completion, participants will be able to:
- Utilize Gemini models to handle multimodal inputs and outputs.
- Implement long-context workflows to facilitate complex reasoning.
- Design 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, please contact us to arrange.
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal capabilities in Vertex AI
- Gemini models and supported modalities
- Enterprise and research use cases
Setting Up the Development Environment
- Configuring Vertex AI for multimodal workflows
- Managing datasets across different 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 within 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 types (text, audio, image)
Audience
- AI researchers
- Advanced developers
- ML scientists
Open Training Courses require 5+ participants.
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