MLOps services: model deployment, monitoring, and retraining pipelines built with the same production discipline we apply to every data pipeline we ship.
Models decay; pipelines without monitoring hide it
Every model's accuracy degrades over time as the real world drifts from its training data. Teams without MLOps discipline find out their model has been quietly wrong for weeks when someone finally checks the same failure mode as an unmonitored data pipeline, just harder to notice because the model still returns an answer, just a wrong one.
What an MLOps build includes
- Model versioning and reproducible training pipelines
- Deployment infrastructure sized for your latency and volume needs
- Performance and drift monitoring with alerting before accuracy silently degrades
- Automated retraining pipelines triggered by drift or on a schedule
- Rollback and canary deployment so a bad model version never reaches all traffic at once
How we work
Treat model deployment as a pipeline with tests, not a one-time export
Instrument prediction quality against ground truth wherever it becomes available
Automate retraining before drift becomes a visible failure
Build rollback paths in from day one, not after the first bad deploy
Typical stack
Frequently asked questions
MLOps applies the same pipeline discipline versioning, testing, monitoring, deployment automation specifically to machine learning models: training runs, model artifacts, and prediction serving. It overlaps heavily with data engineering and typically sits on the same warehouse and pipeline infrastructure we already build.
By monitoring prediction quality against ground truth as it becomes available, and watching for input distribution drift when the data the model sees in production starts looking statistically different from its training data. Both are automatable signals rather than something a team should have to notice manually.
No the right infrastructure depends on your volume and latency needs. A scheduled batch job on a managed service is the right answer for many models; container orchestration earns its complexity at real-time, high-volume serving scale. We size the infrastructure to the actual requirement.