We make AI workloads reliable, repeatable, and observable.
MLOps that reaches production
- Model lifecycle: Versioning (MLflow, DVC, or model registry), promotion gates, and rollback for models and data.
- Training & inference pipelines: Reproducible pipelines on GPU infrastructure — Kubeflow, Argo Workflows, or custom operators.
- GPU observability: Utilisation, memory, throughput, and queue depth via the LGTM stack (Loki, Grafana, Tempo, Mimir) + DCGM.
- Continuous evaluation: Monitor data drift, concept drift, and quality metrics so models stay trustworthy.
- Feature stores: Feast or custom — offline/online consistency, point-in-time correctness, and lineage.
AI operations (AIOps) on solid ground
- Declarative, multi-tenant Kubernetes for ML workloads: Namespace isolation, GPU quotas, priority classes for training vs inference.
- GitOps-managed environments with ArgoCD: Model deployment as code, automated sync, and progressive rollout (canary/blue-green).
- Cost and performance optimisation for GPU fleets: Bin-packing, MIG partitioning, spot/preemptible for batch training, and chargeback/showback.
What we don’t do
- ❌ Lock you into a proprietary MLOps platform.
- ❌ Build custom frameworks when standard tools suffice.
- ❌ Ignore the “ops” in MLOps — we care about paging, runbooks, and SLOs.
Need MLOps help?
We build and run your ML platform. Talk to an engineer.