MLOps and production model deployment
- From notebook to production: packaging, testing and release gates.
- Versioned data, a test set not used to tune, and a rollback you can run.
- Model documentation for audit: assumptions, metrics and limitations.
- No pickle file on a share that only one person can open.
- Monitoring hooks for drift, calibration and operational failure modes after go-live.
Models operations can run and audit
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.
A plant or finance team must be able to rerun the job, see which data version produced the score, and roll back. We do not leave a pickle file on a share and a notebook that only one person can open.
The delivered work is:
- Scientifically sound — test set not used to tune, metrics tied to the decision
- Usable in the target process — batch or API with an owner and a clock
- Efficient to train and run on the hardware you already pay for
- Documented so it can be audited and maintained by your people, not only by us
We combine the practice of data scientists and engineers with ongoing methodological work. Typical domains: manufacturing quality, finance operations, and health or biology studies where the decision owner is named before the first fit.
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.
For computer vision and NLP that must stay up at night, see AI, Machine Learning. For the landing zone and restore path the model runs on, see Cloud Services.
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