AI & ML · MLOps
Ship models like software.
Production-grade MLOps aligned to Google's MLOps maturity model — pipelines, registries, monitoring and governance.
Most ML projects fail not in modelling but in operations. We implement MLOps aligned to Google's MLOps maturity model (levels 0-2) with CI/CD for data and models, versioned artefacts, model registries and continuous monitoring — so every model has a clear owner and a reproducible path from notebook to production.
/ Capabilities
What's in scope.
Training pipelines
Orchestrated with Kubeflow / Vertex AI / MLflow; parameter-versioned and re-runnable with lineage.
Model registry & CI/CD
Registered models with promotion gates, canary deploys, and rollback — models treated as first-class artefacts.
Feature stores
Shared feature definitions with offline / online parity to prevent training-serving skew.
Production monitoring
Prediction distributions, feature drift, ground-truth latency and business KPI tracking with alerting.
From notebooks to pipelines.
Notebooks are fine for exploration; production belongs in pipelines. We help data science teams move to reproducible, source-controlled pipelines without slowing experimentation.
Monitoring closes the loop.
Every deployed model has an owner, an SLO, and dashboards for input drift, prediction drift and business-KPI impact. Retraining triggers are automated, not tribal.
/ Standards & Tooling
Common questions.
Do we need a feature store?
Only when features are shared across many models or when training-serving skew has bitten you. A single-model team can defer this and use good feature pipelines instead.
How is MLOps different from DevOps?
MLOps extends DevOps with data and model versioning, experiment tracking, feature stores, model registries and production monitoring of prediction quality — not just infrastructure health.
Ready to scope a mlops & model lifecycle engagement?
A senior practitioner — not a sales rep — will respond within one business day.
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