Abstract: A method performed by a processing system including at least one processor includes defining a proposal for a proposed machine learning model, identifying an existing machine learning model, where the existing machine learning model shares a similarity with the proposed machine learning model, evaluating a fitness of the existing machine learning model for reuse in building the proposed machine learning model, building a new machine learning model that is consistent with the proposal for the proposed machine learning model by reusing a portion of the existing machine learning model, and monitoring a performance of the new machine learning model in a deployment environment.

Abstract: Aspects of the subject disclosure may include, for example, a drone orchestrator device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of receiving instructions from a workflow manager to perform a job involving a payload; and sending a series of commands to a plurality of drones to orchestrate performance of the job autonomously. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a drone orchestrator device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of receiving instructions from a workflow manager to perform a job involving a payload; and sending a series of commands to a plurality of drones to orchestrate performance of the job autonomously. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

Abstract: A method performed by a processing system including at least one processor includes defining a proposal for a proposed machine learning model, identifying an existing machine learning model, where the existing machine learning model shares a similarity with the proposed machine learning model, evaluating a fitness of the existing machine learning model for reuse in building the proposed machine learning model, building a new machine learning model that is consistent with the proposal for the proposed machine learning model by reusing a portion of the existing machine learning model, and monitoring a performance of the new machine learning model in a deployment environment.

Abstract: Aspects of the subject disclosure may include, for example, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations including selecting modeling logic for an artificial intelligence (AI) model that solves a use case of a plurality of use cases; executing the AI model using holdout data to obtain a sub-result; evaluating the sub-result based on an evaluation metric; and combining the sub-result with other sub-results of the plurality of use cases to determine whether an exit criteria has been met. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.