Abstract: Disclosed are various embodiments for using a distributed ledger to track the evolution or deployment of feature sets for machine-learning. A registration request is received from a first node of a distributed ledger, the registration request comprising a code hash representing a feature set generator and a data hash representing a data set. The registration request is then relayed to a second node of the distributed ledger for approval by the second node. Next, an approval for the registration request is received from the second node. Subsequently, an entry in the distributed ledger comprising the code hash and the data hash is created.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.

Abstract: Systems and methods for the deployment of an application programming interface to cloud-based systems using a distributed ledger are disclosed. The system may include a cloud provider system and a distributed ledger. The cloud provider system may be configured to deploy cloud-based systems and/or APIs to enable one or more participant systems to leverage cloud-based resources. The distributed ledger may record events, functionalities, and operations from the cloud provider system, the participant systems, and the deployed cloud-based systems and APIs.

Abstract: Systems and methods for the deployment of cloud-based systems using a distributed ledger are disclosed. The system may include a cloud provider system and a distributed ledger. The cloud provider system may be configured to deploy cloud-based systems and APIs to enable one or more participant systems to leverage cloud-based resources. The distributed ledger may record events, functionalities, and operations from the cloud provider system, the participant systems, and the deployed cloud-based systems and APIs.

Abstract: Systems and methods for the deployment of cloud-based systems using a distributed ledger are disclosed. The system may include a cloud provider system and a distributed ledger. The cloud provider system may be configured to deploy cloud-based systems and APIs to enable one or more participant systems to leverage cloud-based resources. The distributed ledger may record events, functionalities, and operations from the cloud provider system, the participant systems, and the deployed cloud-based systems and APIs.