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. Leveraging support for long context windows and configurable Gemini API parameters, the platform enables sophisticated applications focused on planning, reasoning, and cross-modal intelligence.
This instructor-led, live training, available online or on-site, targets intermediate to advanced practitioners aiming to design, build, and optimize multimodal AI workflows within Vertex AI.
Upon completion of this training, participants will be equipped to:
- Utilize Gemini models for processing multimodal inputs and generating outputs.
- Implement long-context workflows to facilitate complex reasoning tasks.
- Design pipelines that effectively combine text, audio, and image analysis.
- Optimize Gemini API parameters to enhance performance and ensure cost efficiency.
Course Format
- Interactive lectures and discussions.
- Practical labs focused on multimodal workflows.
- Project-based exercises applied to real-world multimodal use cases.
Customization Options
- For customized training arrangements, please contact us.
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.
- Working with 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 in developing machine learning models.
- Familiarity with multimodal data types, including text, audio, and images.
Audience
- AI researchers.
- Advanced developers.
- Machine learning scientists.
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