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 long context window capabilities and Gemini API parameters, it empowers the creation of advanced applications focused on planning, reasoning, and cross-modal intelligence.
This instructor-led live training, available online or onsite, is designed for intermediate to advanced practitioners looking to design, build, and optimize multimodal AI workflows within Vertex AI.
Upon completing this training, 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 effectively integrate text, audio, and image analysis.
- Optimize Gemini API parameters to enhance performance and cost efficiency.
Course Format
- Interactive lectures and discussions.
- Hands-on labs featuring multimodal workflows.
- Project-based exercises focusing on practical multimodal use cases.
Customization Options
- For customized training requests, please contact us to arrange details.
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal capabilities within 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.
- Applications 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).
Target Audience
- AI researchers.
- Advanced developers.
- ML scientists.
Open Training Courses require 5+ participants.
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