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: Methods and systems for treatment based on thermographic images of a patient to detect temperature patterns indicative of an evolving disease process. Guided by the thermographic image obtained, a treatment means can be positioned on the skin of the imaged body part. The treatment means comprises a medicament that reduces risk factor(s) associated with the progression of the evolving disease process.

Abstract: A system for detecting the risk of breast cancer comprises a thermographic imaging device configured to acquire thermographic images of a female torso; an analysis authority communicatively coupled with the thermographic imaging device, the analysis authority including an input port configured to receive thermographic images from the thermographic imaging device, a database configured to store the thermographic images, a qualitative analysis module configured to automatically perform a qualitative analysis of the thermographic images and generate a qualitative score based on the qualitative analysis, a quantitative analysis module configured to automatically perform a quantitative analysis of the thermographic images and generate a quantitative score based on the quantitative analysis, and a scoring module configured to correlate the qualitative and quantitative scores with a score indicating a risk of breast cancer.