MLOps · 2024
05
Forge
Infrastructure
The brief
Ship models
not just experiments.
A fintech's data science team was producing excellent models — but it was taking 3 months to get any of them into production. By the time a model shipped, the data landscape had shifted and half its value had eroded. The team was building AI for a world that no longer existed by the time it arrived.
KIJO built Forge — a complete ML infrastructure stack with automated CI/CD for models, a central model registry, and an observability layer. What used to take 3 months now takes 3 weeks. The team went from shipping 1 model per quarter to running 12 simultaneous experiments.
Challenge
3 months
to ship a model.
The data science team was skilled and motivated. The problem wasn't talent — it was tooling. Every model deployment was a manual process: hand-crafted Docker images, ad-hoc infrastructure provisioning, no automated testing, no performance monitoring. Engineers spent more time on deployment logistics than on model improvement.
Every model required a custom deployment process — 8-12 engineer-weeks of integration work before any model reached production.
Model iterations weren't consistently tracked — teams lost work, couldn't reproduce results, and struggled to compare approaches.
Once deployed, models ran without monitoring — data drift went undetected until model performance visibly degraded in production.
Solution
ML CI/CD.
Built for speed.
Forge gives the data science team a complete production-grade ML platform. Models are trained, tested, versioned, and deployed through an automated pipeline. A central registry tracks every model, every version, and every performance metric. An observability stack monitors live models and alerts on drift, degradation, and anomalies.
Training → validation → staging → production pipeline with automated tests at each stage — zero manual deployment steps.
Centralised versioned registry with full lineage tracking — every model, every training run, every dataset version, fully reproducible.
Real-time drift detection, performance monitoring, and automated rollback — models stay accurate and the team knows when they don't.
Model to
production
Platform
uptime
More experiments
per quarter
From brief
to live