Abstract: A system for format-agnostic publication of a machine learning model may receive, through an application programming interface (API), machine learning models that have been built, developed, and trained in disparate computing environments, validate and normalize these machine learning models, generate a docker image for each validated and standardized machine learning model, and publish the docker images to a docker registry. The docker images can then be deployed to a managed cluster such as an on-prem managed cluster operating in an enterprise computing environment and/or a managed hyperscale cluster operating in a cloud computing environment. This API-based machine learning model publication approach allows any analytics model developed and trained in any modeling environment be deployed to any managed cluster.

Abstract: A system for format-agnostic publication of a machine learning model may receive, through an application programming interface (API), machine learning models that have been built, developed, and trained in disparate computing environments, validate and normalize these machine learning models, generate a docker image for each validated and standardized machine learning model, and publish the docker images to a docker registry. The docker images can then be deployed to a managed cluster such as an on-prem managed cluster operating in an enterprise computing environment and/or a managed hyperscale cluster operating in a cloud computing environment. This API-based machine learning model publication approach allows any analytics model developed and trained in any modeling environment be deployed to any managed cluster.