MLOps Engineering Services

Foundational MLOps infrastructure — CI/CD for model code, a model registry, automated retraining pipeline, and basic monitoring — typically costs $50,000 to $120,000 and takes 10 to 16 weeks depending on your existing cloud setup and the complexity of your model portfolio. Teams adding MLOps to a pre-existing Codieshub ML engagement often complete it faster since we already understand the data stack.

Yes — we operate in your cloud environment, not a separate one. We assess what you already have (pipelines, registries, monitoring), identify the gaps against production-grade requirements, and fill them using your existing platform's native tools where they are sufficient and supplementing with open-source tooling where they are not. We do not recommend replacing what is working.

We instrument three layers: infrastructure monitoring (latency, throughput, error rates via standard APM), data drift detection (statistical tests on feature distributions against a training baseline, checked on each scoring batch or at configurable intervals), and prediction drift (output distribution shifts that signal concept drift even when features look stable). Alerts route to PagerDuty, Slack, or wherever your team already manages incidents.

Yes — model governance for regulated environments is a specific design target for us, not an afterthought. We implement model registries that capture training data lineage, data preprocessing reproducibility, evaluation results on hold-out sets, approval workflows before production deployment, and prediction logging with configurable retention. This documentation structure is designed to support FDA software guidance, FINRA model risk management expectations, and SOC 2 model change controls.

The answer is a champion-challenger or shadow deployment pattern. The retrained model runs in shadow mode against live traffic — generating predictions that are logged but not served — while the champion model continues serving users. Once the challenger's performance is validated against a predefined threshold over a statistically significant traffic window, it promotes automatically (or pending a human approval gate, depending on your risk tolerance). Rollback is a single configuration change if a model promotes and then underperforms on a metric not captured in pre-promotion eval.