MLOpsModel Lifecycle

The pipeline that keeps models monitored, retrained, and reliable.

Versioned training, automated deployment, monitoring, and drift detection — so the model that worked at launch keeps working a year later, on purpose.

Insight
Models decay quietly. Last year's accuracy, left unmonitored, becomes this year's liability.

Capabilities

CI/CD for ML
Monitoring
Drift detection

What we deliver

A current-state read: the constraints, data, and the decision this work should improve.
The core build — CI/CD for ML, Monitoring, Drift detection — delivered to production quality.
Evaluation, guardrails, and acceptance criteria agreed before launch — not after.
Handoff: documentation, training, and the same team on support afterward.

ML & Data Science

MLOps & Model Lifecycle

Tell us what the first useful version of this should prove, and we'll shape the scope around it.

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