Data & MLOps Platforms — Lakehouse · Vector · MLOps
Models are the easy part. The platform underneath — features, lineage, evals, deploys — is the moat. We build it as a product, not a project.
Feature reuse
>70%
Deploy frequency
12×/week
MTTR for prod model
<30 min
Deliverables
Everything that ships
- Lakehouse layerIceberg/Delta, medallion zones, IaC, cost guardrails.
- Feature storeOnline/offline parity, point-in-time joins, ownership and SLAs.
- Vector DB tierHybrid search, multi-tenant, embedding versioning.
- Model CI/CDReproducible training, eval gates, canary + shadow rollouts.
- Governance packLineage, PII tagging, model cards, audit exports.
Pod composition
- Platform Lead
- Data Engineer
- MLOps Engineer
- Governance SME
Example output · Feature spec · churn.signal_v4yaml
feature: churn.signal_v4
owner: pod.retention
freshness: 15m
sources: [orders.v3, events.web, support.cases]
sla: { availability: 0.999, p95_ms: 80 }
governance: { pii: false, residency: eu }Timeline
Weeks 1–10 · first feature live by week 4
- 1Week 1-2Foundations
Lakehouse + IaC + cost guardrails wired into CI.
- 2Week 3-5Feature + vector tier
Online/offline parity, embeddings pipeline, first model uses it.
- 3Week 6-10CI/CD + governance
Eval gates, canary deploys, lineage, model cards, audit exports.
FAQs
Things prospects ask
Cloud preference?
AWS, Azure or GCP — same blueprint, cloud-native primitives, portable IaC.
Build vs buy?
We compose: open-source core (Iceberg, Feast, MLflow) + managed where it pays.
How does this connect to Data Enablement?
Directly. The Refinery's seven strata land here as governed feature + vector tiers.
Commission · C4 Data & MLOps Platforms
Stand up Data & MLOps Platforms in Weeks 1–10.
We'll respond within one business day with a scoping note, a fixed-price outcome contract, and a named principal. Your details sync straight into our concierge queue.
- • Outcome-priced — no T&M.
- • Sovereign by default — your data, your region, your keys.
- • Wired into the Fuel Pressure gauge from day one.