MLOps and production model deployment
- From notebook to production: packaging, testing and release gates.
- Feature store integration and reproducible experiment tracking.
- Model documentation for audit: assumptions, metrics and limitations.
- Hybrid deployment for computer vision and NLP workloads.
- Monitoring hooks for drift, calibration and operational failure modes after go-live.
Fully Customized Data Science Models Development
We design and implement data science models around the problem, the data you actually have, and the decision the model must support — not around a generic template.
Before training we write down the decision owner, the data lineage, leakage risks and how the score will be used in operations. That keeps the model tied to a process you can run after we leave.
The delivered work is:
- Scientifically sound
- Usable in the target process
- Efficient to train and run
- Documented so it can be audited and maintained
We combine the practice of data scientists and engineers with ongoing methodological work. Typical domains we contribute to:
Each engagement is scoped with domain specialists — we do not hand over a black-box score without assumptions, limits and a validation plan the client team can repeat.
Every engagement is scoped from the data, constraints and decision it must support. We keep a working channel with the client at each stage — from problem framing to validation.
We start with diagnosis: what can be measured, what must not leak, what the process will actually do with a score. Only then do we pick a family of models.
- Problem, data and decision written down before training
- Constraints from the domain, not from a generic playbook
- A validation plan the specialists can run after we leave
Support covers the project itself and the period after handover: monitoring, retraining, and changes when the process or the data shift.
A model that worked on last year’s cohort is not finished work. We leave a path to retrain, to raise an alert when input drift appears, and to change the target when the business question changes.
- Monitoring of inputs, outputs and simple data-quality checks
- Retraining when the process or the population moves
- Handover notes a maintainer can follow without the original author
Quality comes from pairing domain experts with engineers: reproducible pipelines, documented assumptions, and checks against leakage, bias and operational failure modes.
We treat a model as production software: versioned data, a test set that was not used to tune, and an explicit statement of what the score is allowed to decide.
- Reproducible training and evaluation
- Leakage, bias and calibration checks before go-live
- Documentation that an auditor or a new engineer can read