Credit scoring models
Pipelines that retrain monthly, validate on backtests and promote with risk officer approval.
01 / MLOPS
Artificial Intelligence
MLOpsArtificial Intelligence — ML models in production, governed and reliable.
ML models in production, governed and reliable.
We move machine learning models from notebook to business: ML CI/CD pipelines, feature store, model registry, drift monitoring, automated retraining. We turn experiments into reliable systems.
10x
Model deploy velocity
−40%
ML infra cost
100%
Traceable models
§ A
Overview
70% of ML projects never reach production. The problem is rarely model accuracy: it's the lack of infrastructure to consistently manage data, training, deploy, monitoring and governance.
We implement end-to-end MLOps platforms built on open-source and cloud-native tools, focused on reproducibility, audit, costs and security. Every model has traceable lineage from feature to deploy.
§ B
What's included
- Assessment of existing MLOps maturity
- Feature store setup (Feast, Tecton) or offline-first on lakehouse
- Reproducible training pipelines with orchestration (Airflow, Kubeflow, Metaflow)
- Model registry and versioning (MLflow, Weights & Biases)
- CI/CD for models with quality and bias tests
- Scalable serving (KServe, BentoML, SageMaker)
- Drift, performance, fairness monitoring in production
- Governance: model card, lineage, approvals
§ C
Deliverables
What you get at the end — or along the way — of an engagement on MLOps.
- D/01Tailored reference architecture
- D/02IaC pipelines (Terraform) for dev/stage/prod environments
- D/03Project templates and CI/CD
- D/04Observability dashboard
- D/05Operating playbook and team training
§ D
Use cases
Retail recommender system
Real-time feature store, A/B testing, business-metric monitoring.
Demand forecasting
Hundreds of models per SKU/store automatically generated and monitored.
Fraud detection
Low-latency model serving with drift detection on critical features.
§ E
Our process
01
Maturity assessment
Current-state map and 6–12 month roadmap.
02
Foundation
Storage, registry, orchestrator, reproducible environments.
03
Pilot
Migration of an existing model onto the new pipeline as a blueprint.
04
Industrialisation
Onboarding of new models via templates and self-service.
05
Governance
Approval workflows, audit, model risk management.
§ F
Technologies
MLflow · Weights & BiasesKubeflow · Metaflow · AirflowFeast · TectonBentoML · KServe · TritonDVC · PachydermPrometheus · Grafana · Evidently
Indicative stack. We adapt choices to your context, internal skills and existing constraints.
§ G
Frequently asked questions
Q/01Can I use my own cloud?+
Yes. We work cloud-agnostic on AWS, Azure, GCP. We also support on-premise with Kubernetes.
Q/02How long to production?+
A minimal platform is operational in 6–10 weeks. Full industrialisation typically takes 4–6 months.
Q/03What if I don't have internal data scientists yet?+
We can work in a blended team and progressively build internal capabilities.
Next step
Let's talk about mlops.
A 30-minute call to understand your context and whether we can really help. No commitment.