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.

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

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

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.