AI Deployment Platform
HyperDrive is a secure, scalable AI platform created by Hypergiant to accelerate the development and deployment of machine learning models. The platform was designed to solve a core problem in the AI/ML pipeline: organizations often get stuck in experimentation and fail to operationalize models. HyperDrive bridges that gap, enabling data science teams to collaborate, train, evaluate, and deploy models in secure, production-ready environments.
The Challenge
"Many organizations are stuck at the experimentation stage and not deploying models into production environments where they can become valuable to the business."
www.aerospacedefensereview.com
Data science teams often lacked:
- A centralized interface to manage projects and data science workflows
- Visual clarity for model performance across environments
- A system that scaled with both civilian and defense-grade AI needs
Disclaimer: Don't mind the lack of organization in the components, this was pre-variants in Figma :)
THE SOLUTION
My Role
As the lead product designer, I worked with data scientists and ML engineers to:
Deployment Hub
- Gave users control and visibility over real-time model deployment across environments.
- Clear status tags (“FAILED,” “DEPLOYED,” “READY”) with one-click redeploy actions.
- Designed for teams managing multiple live inference endpoints.
Model Inventory
- Card-based and list-based views for ML models across projects and clients.
- Smart filtering and criticality-based sorting for high-priority models.
- Snapshot summaries of versions, metadata, and status indicators.
Dashboard Console
- Built around role-based access control for data scientists, ops teams, and project leads.
- Guided setup: Projects → Data → Models → Monitoring.
- Notifications system showing live model status updates per project.
Evaluation Interface
- Data science-friendly tabs for performance metrics, training stats, and engineering logs.
- Model comparison views with heatmaps and ROC scores.
- Visual indicators for best-performing model candidates.
Design System
- A design system built with modularity, consistency, and classification constraints in mind.
- Built reusable cards, buttons, table elements, and tooltips.
- Created documentation and templates to enable rapid expansion across product teams.
Outcomes
- Decreased model deployment setup time by over 50%
- Built to support collaboration across high-stakes projects spanning aerospace, commercial, and government sectors.
- Built with security and scalability in mind to serve a range of enterprise and defense-focused use cases.
Reflection
HyperDrive wasn’t just about building an AI console—it was about enabling faster, more confident decisions in critical systems.
This project taught me how to balance complexity, modularity, and design in one of the most demanding technical spaces: Engineering the experience of AI at scale—where seamless UX empowers teams to build, train, and deploy mission-critical models with confidence.
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