Moving a model into production is a distinct engineering problem: feature availability at request time, latency budgets, versioning, fallbacks and monitoring for drift.
We package the model behind an API with defined input and output schemas, so any authorized system can use it and you can change the model later without breaking callers.
What the work involves
- Model packaging and version control
- Real-time and batch inference paths
- Feature pipelines and input validation
- Confidence, fallbacks and safe defaults
- Drift, health and prediction logging
Ready to scope it?
Tell us about the data, the callers and the answer you need the API to return.