Abstract: Embodiments are directed towards a machine learning repository for managing machine learning (ML) model envelopes, ML models, model objects, or the like. Questions and model objects may be received by a ML model answer engine. Machine learning (ML) model envelopes may be received based on the questions. The model objects may be compared to parameter models associated with the ML model envelopes. ML model envelopes may be selected based on the comparison such that the model objects satisfy the parameter models of each of the selected ML model envelopes. ML models included in each selected ML model envelope may be executed to provide score values for the model objects and the score values may be included in a report.

Abstract: Embodiments are directed towards classifying data. A machine learning (ML) engine may select an ML model that may employ a cryptographic multi-party computation (MPC) protocol based on model preferences, including a parameter model, provided by a client. A randomness engine may be employed to provide random values and other random values based on the MPC protocol such that the random values may be provided to the client and the other random values may be provided to an answer engine. Input values that correspond to fields in the parameter model may be provided by the client such that the input values may be based on the MPC protocol and the random values. The answer engine may be employed to provide partial results to the question based on the ML model, the input values, and the MPC protocol that may be provided to the client.

Abstract: Embodiments are directed towards a machine learning repository for managing machine learning (ML) model envelopes, ML models, model objects, or the like. Questions and model objects may be received by a ML model answer engine. Machine learning (ML) model envelopes may be received based on the questions. The model objects may be compared to parameter models associated with the ML model envelopes. ML model envelopes may be selected based on the comparison such that the model objects satisfy the parameter models of each of the selected ML model envelopes. ML models included in each selected ML model envelope may be executed to provide score values for the model objects and the score values may be included in a report.

Abstract: Embodiments are directed towards a machine learning repository for managing machine learning (ML) model envelopes, ML models, model objects, or the like. Questions and model objects may be received by a ML model answer engine. Machine learning (ML) model envelopes may be received based on the questions. The model objects may be compared to parameter models associated with the ML model envelopes. ML model envelopes may be selected based on the comparison such that the model objects satisfy the parameter models of each of the selected ML model envelopes. ML models included in each selected ML model envelope may be executed to provide score values for the model objects and the score values may be included in a report.

Abstract: Embodiments are directed towards classifying data. A machine learning (ML) engine may select an ML model that may employ a cryptographic multi-party computation (MPC) protocol based on model preferences, including a parameter model, provided by a client. A randomness engine may be employed to provide random values and other random values based on the MPC protocol such that the random values may be provided to the client and the other random values may be provided to an answer engine. Input values that correspond to fields in the parameter model may be provided by the client such that the input values may be based on the MPC protocol and the random values. The answer engine may be employed to provide partial results to the question based on the ML model, the input values, and the MPC protocol that may be provided to the client.