MLOps Infrastructure
DEPLOYEDThe scaffolding that makes ML teams fast — built so models go from experiment to production without a ticket queue.
Deployment cycle cut from 3 days to 4 hours (18× improvement)
Automated retraining on data drift reduced model staleness incidents by 80%
Experiment tracking adopted by all 8 engineers within one sprint
Canary deployments caught 2 silent regression bugs before full rollout
The team was doing model deployment manually: copy weights to a server, update a config file, restart the service, hope nothing broke. There was no experiment tracking, no rollback mechanism, and no way to tell if a new model was actually better than the one it replaced. Deployments took 3 days because every step required coordination across three people.
Built on MLflow for experiment tracking and model registry, Apache Airflow for orchestrating training pipelines, and Kubernetes for deployment. Models are promoted through environments (dev → staging → canary → production) via automated gates that evaluate performance on a held-out validation set. Failed promotions trigger automatic rollback within 2 minutes.
Integrated Evidently for continuous data and model monitoring. When feature distributions drift beyond a configurable threshold, the system automatically queues a retraining run and notifies the team. The first month of operation it caught a schema change in an upstream data source that would have silently corrupted all downstream features.
MLOps tooling is only as good as the workflows it replaces. The most important work wasn't writing the platform — it was sitting with the team and documenting how they actually deployed models, then designing automation around those real steps rather than theoretical best practices. Platform adoption was 100% within two weeks because it fit how people already thought about the problem.