Abstract: The subject matter of this disclosure relates to systems and methods for monitoring and managing machine learning models and related data. Histogram structures can be used to aggregate streams of numerical data for storage and metric calculations. Drift in such data can be identified and monitored over time. When significant drift is detected and/or when model accuracy has deteriorated, models can be automatically refreshed with updated training data and/or replaced with one or more other models. A model controller is used to automate model monitoring and management activities across multiple prediction environments where models are deployed and prediction jobs are executed.

Abstract: Comparing a challenger model with a primary model is provided herein. In an embodiment, a system comprises one or more processors, coupled to memory, configured to determine, based on a comparison of a first model that is deployed as a primary model with a second model that is acting as a challenger model, that the second model performs better than the first model based on at least one performance metric; determine, based on a comparison of a characteristic of the first model with a characteristic of the second model, to skip a validation process for the second model; and establish the second model as the primary model in the deployment to replace the first model in the deployment.

Abstract: The subject matter of this disclosure relates to systems and methods for monitoring and managing machine learning models and related data. Histogram structures can be used to aggregate streams of numerical data for storage and metric calculations. Drift in such data can be identified and monitored over time. When significant drift is detected and/or when model accuracy has deteriorated, models can be automatically refreshed with updated training data and/or replaced with one or more other models. A model controller is used to automate model monitoring and management activities across multiple prediction environments where models are deployed and prediction jobs are executed.

Abstract: A compact, strengthened rotor assembly of a radiator fan, the radiator fan having a baseplate, a stator assembly, a rotor assembly and a reverse axle. The reverse axle is erected at the center of the baseplate or stator assembly and protruded upwards. The rotor assembly has a hub with a top wall and a circumferential wall. A magnetic ring is set annularly into the circumferential wall. Several blades are set annularly onto the exterior of the circumferential wall. A metal sleeve is located at the center of the top wall and protruded downwards. A mating portion is set at the top of the metal sleeve for mating with the top wall. A holding portion is formed within the metal sleeve for assembly and positioning of a bearing, and the bearing is used for pivoting of the reverse axle. The thickness of the top wall ranges between 0.2 mm and 0.5 mm.

Abstract: A compact, strengthened rotor assembly of a radiator fan, the radiator fan having a baseplate, a stator assembly, a rotor assembly and a reverse axle. The reverse axle is erected at the center of the baseplate or stator assembly and protruded upwards. The rotor assembly has a hub with a top wall and a circumferential wall. A magnetic ring is set annularly into the circumferential wall. Several blades are set annularly onto the exterior of the circumferential wall. A metal sleeve is located at the center of the top wall and protruded downwards. A mating portion is set at the top of the metal sleeve for mating with the top wall. A holding portion is formed within the metal sleeve for assembly and positioning of a bearing, and the bearing is used for pivoting of the reverse axle. The thickness of the top wall ranges between 0.2 mm and 0.5 mm.