MLOps platform build · 16 weeks · Any Kubernetes
A vendor-neutral ML platform on your Kubernetes. Operated by your team on day one.
Model registry, automated retraining, drift monitoring, canary deployment, auto-rollback, full lineage, regulator-grade governance — stood up on any cloud or on-prem in 16 weeks. Fixed fee. No staff-aug tail.
CNCF Certified · ISO 27001 · SOC 2 Type II · Databricks Partner · Azure AI/ML Specialisation
Build
lane 1Deploy
lane 2Observe
lane 3Mean time-to-production
6.0days
across 14 deployments
11 → 6
Weeks → days median time-to-production
14
Hardened deployments across UAE / KSA
16 weeks
Fixed-fee MLOps platform build delivery
113
Days until your team operates without us
The five symptoms of an MLOps estate that has stalled
Pick the ones you recognise. We have built the runbook for all five.
Most enterprises do not have a model problem — they have an operating problem. Here is the diagnostic our principal platform engineer runs in the first 30 minutes of every architecture review.
Symptom 01
No registry
Nobody can list every production model with a single SQL query, who owns it, what it was trained on, or what version was promoted last Tuesday. Production becomes folklore.
Symptom 02
No retraining
Every retrain is a Jira ticket and a hero engineer. Models age. Calibration drifts. The first time you find out is when the business calls.
Symptom 03
No drift signal
Data quality, feature distributions, prediction distributions, business KPIs — none are monitored in one place with thresholds that mean anything. The dashboard is decorative.
Symptom 04
No rollback
A bad model promotion takes the team 6 hours to undo. There is no shadow-mode, no percentage canary, no auto-rollback. Every promotion is an act of courage.
Symptom 05
No governance pack
When the regulator asks for the model card, lineage, eval evidence, and incident history — the team spends two weeks rebuilding it from Jira tickets and Slack threads.
If you recognise three or more
Book the architecture review
Sixty minutes with the principal platform engineer. We come prepared on your stack — bring your current architecture diagram and your three biggest production headaches.
Book the review →The Brocode reference platform
Eleven components. Boring on purpose. Hardened across 14 deployments.
A deliberately mainstream stack. Each component is an industry default with an active OSS community. There is no Brocode-proprietary critical-path component — your SRE team can patch every layer.
- L01MLflowModel registry, experiment tracking, model lineage
- L02BentoMLModel packaging & serving with auto-batching
- L03Ray ServeAutoscaling, multi-model routing, GPU sharing
- L04FeastFeature store — batch + online, point-in-time correctness
- L05Airflow / PrefectOrchestration for retraining and feature jobs
- L06Arize / EvidentlyDrift, quality, and KPI monitoring with thresholds
- L07Great ExpectationsData-quality contracts gating training + serving
- L08DVC + LakeFSDataset versioning and reproducible training inputs
- L09Argo CD + GitLab CIGitOps deployment for models and infra
- L10Kyverno + OPA GatekeeperPolicy as code — admission control and governance
- L11Prometheus + Grafana + LokiPlatform observability across the cluster
The Canary Deployer pattern
Shadow → percentage canary → full rollout — with auto-rollback gated on KPI.
The pattern that neither Databricks nor SageMaker ship by default. Every model promotion runs the same lifecycle, every rollback is automatic, every KPI window is configurable per model.
Stage 1 · Shadow
New model receives mirrored traffic
Mirrored request stream. New model predictions logged. Zero customer impact. Run for 24–72 hours depending on traffic class. Calibration delta against incumbent published in the registry.
Stage 2 · Canary
5 % → 25 % → 50 % live traffic
Gradual traffic split. Each step gated on KPI window: calibration error, false-negative rate, conversion delta — chosen per model. Argo Rollouts + Arize gates the next step or rolls back.
Stage 3 · Auto-rollback
Revert if the gate breaches
If the 7-day calibration drift or any configured KPI window breaches, the rollout reverts to the prior model — automatically, with a registry entry and an incident draft. No 3 AM pager required.
16-week MLOps platform build
Week-by-week. Fixed fee. Named senior engineers on the SoW.
The plan we have run 14 times. Adjusted to your existing estate but never re-scoped halfway through — the SoW is the contract, not a starting position.
Weeks 1–4
Discovery & landing zone
Existing estate audit. Target architecture agreed with your platform, security, and data teams. Landing-zone provisioned on your chosen Kubernetes — EKS, AKS, GKE, OKE, OpenShift, G42 Core42, or vanilla kubeadm.
Architecture signed by your CTO & CISO
Weeks 5–10
Platform install + 3 reference models migrated
MLflow registry, BentoML + Ray Serve, Feast, Airflow / Prefect, Arize / Evidently, Great Expectations, Argo CD, OPA, Prometheus / Grafana / Loki. Three of your existing models migrated end-to-end through the canary pattern.
3 models in registry, serving on canary
Weeks 11–14
Production hardening + runbooks
SLOs, on-call rotation design, disaster-recovery plan, governance pack templates, auto-rollback gates configured per model, EU AI Act / SAMA / CBUAE evidence templates wired into the registry.
Governance pack reviewed by Risk
Weeks 15–16
Enablement & SRE handover
Your engineers shadow our team, then run the platform under our watch, then we sign off. The engagement does not close until your SRE lead countersigns the runbook audit.
Customer team operates on day 113
Side-by-side
Brocode vs Databricks, SageMaker, W&B, and Big-4 MLOps practices.
What each option actually delivers. Not a marketing maturity matrix — operational delta on the things your platform team cares about.
| Capability | Brocode | Databricks | AWS SageMaker | Weights & Biases | Big-4 MLOps |
|---|---|---|---|---|---|
| Runs on any Kubernetes (cloud, on-prem, sovereign) | Databricks only | AWS only | Component only | Slideware | |
| Model registry, retraining, drift, canary — all in one platform | Inside Databricks | Inside SageMaker | |||
| Auto-rollback on KPI drift Our proprietary Canary Deployer pattern. | |||||
| EU AI Act / SAMA / CBUAE model card generation | Auto-generated | Manual | Manual | Manual | |
| Customer owns and operates the platform on day one | Vendor-managed | Vendor-managed | Staff-aug forever | ||
| Fixed-fee delivery | 16 weeks | T&M | T&M | Tool licence | T&M / open-ended |
| Senior engineers named on the SoW | Variable | Variable | Vendor SE | Junior-heavy |
The three objections from your platform lead
What gets raised in week one of every architecture review.
Objection 1
We have a Databricks / SageMaker estate already. We cannot rip and replace.
We do not rip. The Brocode platform treats Databricks and SageMaker as model sources — your data scientists keep training there. The registry, canary, drift, and governance plane sits on your Kubernetes alongside, not on top.
Objection 2
We have tried this before. The Kubeflow build died after six months.
Mainstream stack only, named senior engineers on the SoW, fixed fee, and a dedicated 2-week handover where your SRE lead has to countersign the runbook. If they refuse, the engagement does not close.
Objection 3
Show me the run-cost. Our finance team will not approve opaque TCO.
Per-model run-cost lands in the AED 380–1,200 / month band across 14 deployments. The lead-magnet pack includes the actual TCO calculator and the node-sizing runbook we use with customers.
Anonymised references
Three live platforms. Each available in full under NDA.
UAE tier-1 bank
23 models migrated from notebooks and one-off containers to a registry-driven platform in 14 weeks. Mean-time-to-production reduced from 11 weeks to 6 days. CBUAE-aligned model-governance pack auto-generated for every promotion. The platform team owns it on day one.
11 weeks → 6 days TTPM
Regional insurer
Claims-fraud model retrained weekly via Airflow + MLflow + Arize. False-negative drift detected in 3 days (prior baseline: 6 weeks). Two annual SAMA-aligned model reviews now generated from the registry rather than rebuilt from tickets.
Drift detection × 14 faster
Energy major
Predictive-maintenance estate — 47 models across 6 production assets — consolidated on a single Ray Serve cluster. GPU cost down 38 %. Eight engineers reallocated from model-firefighting to new use cases.
GPU spend −38 %
Bank deployments cover Banking & Financial Services. For data-foundation work, see Data Engineering for AI.
Free download
The MLOps Reference Architecture Pack — 4 Blueprints, Cost Models, Runbooks
A 60-page technical pack covering AWS EKS, Azure AKS, on-prem OpenShift, and G42 Core42 reference architectures — plus Terraform / Helm references and a TCO calculator (Google Sheet).
- Reference architecture 1 — AWS EKS with managed services
- Reference architecture 2 — Azure AKS baseline
- Reference architecture 3 — On-prem OpenShift / vanilla Kubernetes
- Reference architecture 4 — Sovereign / hybrid (G42 Core42)
- TCO calculator: per-model run-cost, GPU sizing, request volume
FAQ
What platform leads and CIOs ask first.
Eight questions our principal engineers answer in nearly every architecture review.
Nothing gets ripped. Our platform treats Databricks and SageMaker as model sources — your data scientists keep training there if that is what works. The registry, the canary deployer, the drift monitoring, the governance plane sits on your Kubernetes and ingests model artefacts from both. Most of our 14 deployments are coexistence builds. You stop being locked-in to either vendor for the operational plane; if you ever leave Databricks, the platform keeps running.
Architecture review
Sixty minutes with our principal ML platform engineer.
Six fields — models in production, current tooling, target hosting, top pain, regulators in scope, team size. We arrive at the call with a draft reference architecture for your stack and three named models we would migrate first.
Continue exploring
Related capabilities and stories
Data Engineering for AI
Feature pipelines and data-quality contracts under the platform.
Read moreSelf-hosted LLM Infrastructure
The platform extended to host sovereign LLMs.
Read moreAI Consulting & Strategy
Roadmap-led MLOps work for AI committee mandates.
Read moreBanking & Financial Services
Tier-1 bank platform builds with SAMA / CBUAE governance.
Read moreDocument Intelligence
The OCR estate running on the platform backbone.
Read more