Multimodal LLM Workflows in Vertex AI Training Course
Vertex AI delivers robust infrastructure for developing multimodal large language model workflows, unifying text, audio, and visual data within a cohesive operational pipeline. By leveraging long-context capabilities and specific Gemini API configurations, this platform facilitates sophisticated applications involving strategic planning, complex reasoning, and cross-modal intelligence. This instructor-led session, available through online or onsite delivery models, targets intermediate to advanced practitioners seeking to design, construct, and optimize multimodal AI systems within the Vertex AI environment.
Upon completion of this training, participants will demonstrate the ability to:
* Utilize Gemini models to process and generate multimodal inputs and outputs.
* Deploy long-context workflows to support intricate reasoning tasks.
* Architect pipelines that seamlessly integrate text, audio, and image analysis.
* Tune Gemini API parameters to maximize performance and ensure cost-effective resource allocation.
**Course Format**
* Interactive instruction coupled with structured discussion.
* Practical laboratory exercises focused on multimodal workflow implementation.
* Applied project-based assignments to address real-world multimodal use cases.
**Course Customization**
Organizations requiring specialized instruction tailored to specific operational needs may contact the provider to arrange customized training services. This curriculum is designed to support the specific requirements of federal agencies and is intended for government use.This course is available as onsite live training in US Government or online live training.
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Course Outline
Introduction to Multimodal Large Language Models in Vertex AI
- Overview of multimodal capabilities within the Vertex AI platform
- Overview of Gemini models and supported modalities
- Enterprise and research use cases for government applications
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 for government
Evaluation and Iteration
- Testing multimodal performance
- Metrics for accuracy, alignment, and drift
- Hands-on lab: evaluating multimodal workflows
Summary and Next Steps
Requirements
- Competency in Python programming languages
- Proven track record in the development of machine learning models
- Operational knowledge of multimodal data structures, including textual, auditory, and visual inputs
Target Stakeholders
- Artificial intelligence research personnel
- Senior software engineering professionals
- Machine learning science specialists
This capability is designed specifically for government agencies seeking advanced technical expertise.
14 Hours
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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