MLOps Development Services

MLOps is the engineering discipline that makes machine learning reproducible, deployable, and monitorable in production. Without it, models stall in notebooks, degrade silently, or become impossible to audit when something goes wrong.

DevOps practices still apply, but ML adds data/version lineage, training reproducibility, feature consistency, and statistical monitoring. Treating models like ordinary microservices misses the failure modes that matter.

A production baseline for one reference pipeline typically takes 8–14 weeks. Multi-team platform programmes with feature stores and full migration roadmaps often run 4–6 months in phases.

Reference pipeline and monitoring engagements commonly range from $80,000 to $250,000. Broader platform builds with feature stores and multi-cloud serving are scoped by team count and compliance depth.

We select against your existing cloud and skills. Common stacks include MLflow or cloud-native registries, Kubernetes or managed ML platforms for training/serving, and open monitoring where it fits. We avoid tool-first rewrites when your stack can be hardened.

Yes. Many engagements start by automating deploy/rollback and adding drift monitoring for the highest-value models, then expand toward a shared platform once the pain is measurable.

Shared feature definitions, point-in-time correct training sets, and parity tests between offline and online paths. Feature stores help, but process and tests matter as much as the store itself.

MLOps provides the technical controls that governance depends on — lineage, promotion gates, monitoring. Policy ownership and risk tiers sit in AI governance. We often deliver them together when both gaps are clear.

If you are unsure whether the bottleneck is data, talent, use-case selection, or platform, start with an AI readiness assessment. If models already exist and deploys are the pain, we scope MLOps directly.

Yes. We design self-hosted registries, training, and serving for organisations that cannot use public cloud ML services, including restricted network and secrets patterns.

Platforms need ownership for pipeline reliability, cost, and monitoring thresholds as models evolve. We can hand off to your platform team or provide a scoped retainer for operations and backlog grooming.

When we build models, we ship them onto the same MLOps patterns. For teams that already have data scientists, we focus on the platform and leave model science with your team.