Results for "pipelines"

Protecting data during network transfer and while stored; essential for ML pipelines handling sensitive data.

Model trained to predict human preferences (or utility) for candidate outputs; used in RLHF-style pipelines.

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.

Central system to store model versions, metadata, approvals, and deployment state.

Time from request to response; critical for real-time inference and UX.

Incrementally deploying new models to reduce risk.

Coordinating models, tools, and logic.