Abstract: Systems and methods provide a HybridOps model for the identification, capture, isolation, feature engineering and adjudication of source signal data signatures for inclusion in calibration quality standard reference signal data signature libraries that improve machine learning and validation, reduces model bias and reduces model drift. The HybridOps model may include an “unlocked” AI/ML (machine learning enabled) public facing deployment pipeline in parallel with a clone AI/ML deployed in an internal development environment using a ML-Ops pipeline and in parallel with a clone “locked” AI/ML (machine learning disabled) as a standard reference. The three deployed models enables monitoring and measuring model drift, context drift and product progression for improved verification and validation of model reliability.

Abstract: An apparatus for turning a hard and/or brittle material includes a precision workpiece spindle, a workpiece support, a hard and/or brittle workpiece rigidly coupled to the workpiece support, and a diamond tool blade rigidly coupled to a spin-turner mechanism. The diamond tool blade is formed to include a cutting edge arranged to confront the hard and/or brittle workpiece to remove material from the workpiece to form precision-turned components such as optical lenses, mirrors, and optical molds.

Abstract: An apparatus for turning a hard and/or brittle material includes a precision workpiece spindle, a workpiece support, a hard and/or brittle workpiece rigidly coupled to the workpiece support, and a diamond tool blade rigidly coupled to a spin-turner mechanism. The diamond tool blade is formed to include a cutting edge arranged to confront the hard and/or brittle workpiece to remove material from the workpiece to form precision-turned components such as optical lenses, mirrors, and optical molds.