MLOps & LLMOps
Ship and run models with confidence.
CI/CD for models and prompts, automated evaluation, drift and cost monitoring, and safe rollouts — the full lifecycle, from experiment to governed production.
CI/CD for models and prompts, automated evaluation, drift and cost monitoring, and safe rollouts — the full lifecycle, from experiment to governed production.
Automated pipelines, evaluation gates and monitoring across the full ML and LLM lifecycle — with governance your auditors will accept.
Every model and prompt change moves through an automated pipeline — tested, versioned and reproducible.
Versioned models, datasets and artifacts — you always know exactly what is running, and what ran before.
Ragas and LLM-as-judge quality gates score every release candidate before it can reach users.
Canary releases, A/B comparisons and one-click rollback — regressions never reach the whole fleet.
Dashboards and alerts for data drift, model quality, token cost and latency — issues surface before users notice.
RBAC, audit trails and full reproducibility — plus incident response runbooks when something does go wrong.
Automated CI/CD, serving, evaluation and monitoring — safe, reproducible model delivery from training to production.
Feature pipelines, training runs and prompt changes flow through CI/CD into a versioned registry.
Every candidate is scored by an automated eval harness; releases that miss the quality bar never leave staging.
Canary and A/B rollouts expose new versions to a slice of traffic first, with one-click rollback if metrics dip.
Drift, quality, cost and latency dashboards run continuously, with alerting and incident runbooks ready.
Safe, monitored, reproducible model delivery from training to production — for classical ML and LLM systems alike.
A fixed-scope path from ad-hoc releases to a governed, automated lifecycle.
We map how models and prompts ship today, and identify where regressions, drift and cost leaks originate.
CI/CD and an eval harness running on one real model or LLM application, with quality gates enforced.
Canary rollouts, drift and cost monitoring, and governance in place — operated by your team.
Full case study below — including how the eval harness blocks regressions before they reach a single user.
Manual, ad-hoc LLM releases risked regressions, prompt drift and runaway token cost — with no safety net.
Built CI/CD for models and prompts with an automated eval harness (Ragas + LLM-as-judge), canary rollouts, one-click rollback and cost and drift monitoring.
Confident, frequent releases with quality gates enforced automatically.
Forecasting models went stale fast and required painful manual retraining, hurting inventory decisions.
Automated feature pipelines with a feature store, scheduled retraining, monitoring and alerting — a fully hands-off MLOps loop.
Always-fresh models and dramatically less manual toil for the data team.
Tell us how your models reach production today — we'll return a lifecycle assessment and automation plan in days.