> For the complete documentation index, see [llms.txt](https://optiview-1.gitbook.io/optiview-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://optiview-1.gitbook.io/optiview-docs/tech/ml-lifecycle-and-online-inference-mlops.md).

# ML Lifecycle & Online Inference (MLOps)

<figure><img src="https://2222255890-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FywRbPvnDR41TjbXWb0nX%2Fuploads%2FhismhoXYAELViSQRM8zf%2Funknown.png?alt=media&amp;token=00fee669-db08-4c34-b950-4aee49f72b12" alt=""><figcaption></figcaption></figure>

* **Offline training** using historical OHLCV data, on-chain metrics, and sentiment features\
  with walk-forward validation and cross-validation
* **Model registry** for versioning, metrics tracking, and rollback-ready artifacts\
  (container images and model weights)
* **Deployment strategy** with canary releases and A/B testing
* **Online monitoring** for data drift, concept drift, confidence anomalies, and\
  deviations from target risk/return metrics, triggering retraining when needed

> This architecture enables OptiView to remain **scalable, explainable, and secure**, while continuously improving intelligence and user experience.