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Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc
Takeaway
A clear research handoff document and repeatable service architecture help frontier ML prototypes become maintainable production features.
Summary
- Higharc's home-building product combines computer vision, custom transformers, diffusion, and reasoning agents, requiring a careful handoff between researchers and production engineers.
- A research prototype taxonomy document records domain representations, business value, type contracts, persistence, architecture, and the path to integration.
- A Python monorepo isolates research capabilities into decoupled microservices behind a gateway, with API, business-logic, and data layers.
- FastAPI services, reproducible build metadata, dependency locks, and shared CI checks give research prototypes a consistent production structure.
ml-productionmicroservicesresearch-engineering
Original description
Three tactical tips to speed up how quickly your R&D team can turn novel research into customer-ready features Speakers: Vaidas Razgaitis (Higharc): Vaidas is a Senior Research Engineer at Higharc, where he specializes in turning frontier ML research into production-grade features. X/Twitter: https://x.com/gingiVaidas LinkedIn: / vrazgaitis GitHub: https://github.com/VRazgaitis