MLOps Pipeline Diagram
Trace the flow from training and CI/CD to deployment, monitoring, and retraining.
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An MLOps pipeline diagram shows how machine learning models move from development into reliable production operation. It centers on an automation orchestrator linking data and model versioning, CI/CD, a model registry, deployment to a serving layer, production monitoring, and a feedback loop that triggers automated retraining when drift is detected.
ML engineers, platform teams, and DevOps practitioners use this MLOps diagram to standardize model delivery, govern releases, and keep models accurate over time. It is a strong reference for designing an MLOps pipeline, planning CI/CD for ML, or explaining continuous training to stakeholders.
Great for
- MLOps platform design
- CI/CD for ML planning
- Model governance reviews
- Continuous training docs
- Engineering onboarding
Frequently asked questions
What is an MLOps pipeline?+
It is an automated workflow that takes machine learning models from training through CI/CD, deployment, and monitoring, with a feedback loop that retrains models as data changes.
What are the components of MLOps?+
Core components include data and model versioning, CI/CD automation, a model registry, a serving layer, production monitoring with drift detection, and an automated retraining loop.
How is MLOps different from a training pipeline?+
A training pipeline produces a model from data, while MLOps wraps deployment, monitoring, governance, and continuous retraining around that model to keep it reliable in production.
What is model drift monitoring?+
It tracks how production data and predictions diverge from training conditions, alerting teams or triggering retraining when accuracy degrades due to changing data.
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