Tools & technologies
Deployment and MLOps
Taking the model to production and keeping it alive: an inference API, containers, CI/CD, a version registry and drift monitoring.
Taking the model to production and keeping it alive: an inference API, containers, CI/CD, a version registry and drift monitoring.
Serving with guarantees
The same pipeline that trains is the one that serves, so preprocessing is identical. Schema-based input validation, decisions tied to a cost-based threshold and the model version recorded in every response.
Watching for drift
A reference profile of the training data and drift metrics (PSI) over live traffic, exposed to Prometheus and Grafana. You learn the model is degrading before the business does.
Operating locations
Studio in Madrid
Based in Madrid, working remotely with brands, studios and agencies inside and outside Spain.
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Decision flow
Train and serve, the same pipeline.
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Friction map
Cost-based decisions.
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Journey shape
Drift monitored.
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Let's build something that reaches production.
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