ML infra that doesn't fight you.
Pragmatic pipelines, serving, and observability — sized to your team.
Most MLOps stacks are over-engineered for the team using them. We build the right amount of infrastructure for where you actually are — pipelines that run, models that serve, alerts that fire when things drift, dashboards your team will open.
Engineering, not slides.
Data Pipelines
Batch + streaming ingestion. Schema evolution handled. Backfills supported. The stuff that breaks at 3am, designed not to.
Feature Engineering
Feature stores when warranted, plain Postgres tables when they're not. We don't reach for tools you don't need.
Model Training Pipelines
Reproducible training runs, versioned data + code + weights. Experiment tracking and registry built in.
Model Serving
Real-time inference, batch scoring, edge deployment. Auto-scaling tuned to your traffic shape.
Drift Detection
Feature drift, prediction drift, label drift. Alerts before customers tell you something's wrong.
Cost Observability
Per-model spend, per-customer spend, per-inference cost. The dashboard that catches the surprise bill.
From idea to production.
Assessment
Map your current stack, identify failure points, prioritize fixes by leverage.
Minimum-viable infra
Build only what you need to safely ship. Add complexity only when it has a job.
Monitoring before scaling
Observability ships before any new model goes live. You can't operate what you can't see.
Handoff & runbook
Your team owns it. We write the runbooks, train your on-call, and step out.
Models & tools we reach for.
Common questions.
Do we need Kubernetes?
Probably not. Managed services (Modal, SageMaker, Vertex, Replicate) often beat self-hosted K8s for early-stage ML. We pick what fits team size.
We have a single ML engineer — what's right-sized?
Boring tools, opinionated defaults, and tons of monitoring. We optimize for a small team's cognitive load.
Can you migrate us off a broken stack?
Yes. Common scenario. We do migrations incrementally with parallel runs and clean cutover criteria.
Let's scope it together.
Free 30-minute call. Bring your problem statement and current stack — we'll tell you honestly whether it's worth the build.