Multimodal LLM Workflows in Vertex AI Training Course
Vertex AI equips developers with robust tools to construct multimodal Large Language Model (LLM) workflows, seamlessly integrating text, audio, and image data into unified pipelines. Leveraging extended context window capabilities and Gemini API parameters, the platform facilitates sophisticated applications in planning, complex reasoning, and cross-modal intelligence.
This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners seeking to design, build, and optimize multimodal AI workflows within the Vertex AI ecosystem.
Upon completion of this training, participants will be able to:
- Utilize Gemini models to handle multimodal inputs and generate corresponding outputs.
- Develop long-context workflows to address complex reasoning challenges.
- Architect pipelines that effectively integrate text, audio, and image analysis.
- Optimize Gemini API parameters to enhance performance while ensuring cost efficiency.
Course Format
- Interactive lectures and facilitated discussions.
- Practical, hands-on labs focused on multimodal workflows.
- Project-based exercises demonstrating applied multimodal use cases.
Course Customization Options
- To request customized training for this course, please contact us to arrange a 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 in developing machine learning models
- Familiarity with multimodal data types (text, audio, image)
Audience
- AI researchers
- Senior software developers
- Machine learning scientists
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