Data science: from prototype to product

Data science often starts in a notebook or script. Turning that into a product feature—recommendations, risk scores, forecasts—requires engineering: APIs, batch or real-time pipelines, and integration with the rest of your stack.

We help data scientists and engineers work together: clear contracts (inputs, outputs, SLAs), versioning, and monitoring so that when something breaks, you know why. We also design for iteration: the first version might be a heuristic; the next might be a model. The system should support both.

We often see teams stall at the handoff between research and production. We define a thin interface—inputs, outputs, and SLAs—so that data science can iterate on the model while engineering owns the pipeline and deployment. That separation keeps both sides moving.

If you're scaling data science and need a path from prototype to production, we'd be happy to share patterns that have worked for our clients.