We build the platforms that ML teams need to ship and operate models reliably, including feature stores, evaluation pipelines, and model registries.
Production tooling for ML teams.
PANEOTECH builds the platforms that ML teams need to ship and operate models reliably. Our MLOps practice covers experiment tracking, model registries, training and deployment pipelines, monitoring and observability, and the governance frameworks that institutional ML deployment requires. We build the platform that makes ML a sustained capability, not a series of bespoke deliverables.
Across the MLOps and ML platform stack.
Experiment tracking
Versioned experiment logs with full reproducibility, lineage from data to model, and the workflow that lets data scientists move fast without losing the audit trail.
Feature stores
Centralised feature engineering with online and offline parity, freshness guarantees, and the governance that prevents the silent feature drift that breaks production models.
Model registry
Versioned model storage, metadata, lifecycle states (staging, production, archived), and the promotion workflows that turn a model from an artifact into a production asset.
Training pipelines
Reusable training infrastructure with reproducible environments, distributed training where it pays, and the orchestration that scales from one model to a portfolio.
Deployment pipelines
Model serving, canary deployments, A/B testing infrastructure, rollback safety, and the deployment patterns that match the risk profile of the institutional decision the model supports.
Monitoring and drift
Production model observability, drift detection, performance regression alerting, and the closed-loop retraining workflows that keep models healthy across their full lifecycle.
ML platforms compound.
An ML team without a platform ships models slowly and operates them reactively. An ML team with a platform ships models faster and operates them proactively, and the platform itself becomes the most valuable asset on the team. Our practice exists to build that asset, with the engineering discipline that makes the second model and the tenth model both cheaper than the first.
