Multimodal LLM Workflows in Vertex AI Training Course
Vertex AI offers robust tools for creating multimodal LLM workflows that seamlessly integrate text, audio, and image data into a unified pipeline. Supported by long context window capabilities and Gemini API parameters, it facilitates 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 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 to handle multimodal inputs and outputs.
- Implement long-context workflows to enable 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 focused on multimodal workflows.
- Project-based exercises addressing applied multimodal use cases.
Customization Options
- To request customized training for this course, please reach out to us to make arrangements.
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 types (text, audio, image)
Audience
- AI researchers
- Advanced developers
- ML scientists
Open Training Courses require 5+ participants.
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