Abstract: A model validation system is described that is configured to automatically validate model artifacts corresponding to models. For a model artifact being validated, the model validation system is configured to dynamically determine the validation checks to be performed for the model artifact, where the validation checks include various validation checks to be performed at the model artifact level and also for individual components included in the model artifact. The checks to be performed are dynamically determined based upon the attributes of the model artifact and of the components within the model artifact. The system is configured to generate a validation report that comprises information regarding the checks performed and the results generated from performing the various validation checks. The validation report may also include information suggesting actions for passing checks that result in a failed check, or for improving the scores of certain validation checks.

Abstract: A model validation system is described that is configured to automatically validate model artifacts corresponding to models. For a model artifact being validated, the model validation system is configured to dynamically determine the validation checks to be performed for the model artifact, where the validation checks include various validation checks to be performed at the model artifact level and also for individual components included in the model artifact. The checks to be performed are dynamically determined based upon the attributes of the model artifact and of the components within the model artifact. The system is configured to generate a validation report that comprises information regarding the checks performed and the results generated from performing the various validation checks. The validation report may also include information suggesting actions for passing checks that result in a failed check, or for improving the scores of certain validation checks.

Abstract: Embodiments secure data on a cloud based network that comprises one or more machine learning (“ML”) notebooks. Embodiments monitor activity on each of the ML notebooks, the activity including one or more commands. Embodiments classify each of the commands, the classifying including generating input parameters. Based on the input parameters, embodiments determine a risk score for each of the ML notebooks. When the risk score exceeds a predetermined threshold, embodiments generate an alert.

Abstract: Embodiments prevent a reverse engineering attack on a machine learning (“ML”) model. Embodiments receive a first set of requests from a plurality of users to the ML model. Based on the first set of requests, embodiments identify a first user attempting to attack the ML model and, in response to the identifying, generate a shadow model that is similar to the ML model. Embodiments receive a second set of requests from the first user to the ML model and, in response to the second set of requests, generate an ML model set of responses and a shadow model set of responses. Embodiments compare the ML model set of responses with the shadow model set of responses and, based on the comparison, determine whether the first user is attempting the reverse engineering attack on the ML model.

Abstract: The present embodiments relate to an event attribution for estimating a downstream impact for a computing device of a cloud computing system. The computing device can transmit a data schema to a first client application executing on a first web server, the data schema describing a plurality of event data instance characteristics. The computing device can receive a first event data instance and a second event data instance from the first client application. The computing device can format the first event data instance and the second event data instance to conform to a uniform format as described by the data schema. The computing device can link the first event data instance and the second event data instance based at least in part on an event data instance characteristic. The computing device can calculate an attribution score between the first event data instance and the second event data instance.

Abstract: Embodiments securely share a machine learning (“ML”) notebook, comprising a plurality of cells, over a cloud network. Embodiments receive the ML notebook with one or more of the cells designated as a masked cell. Embodiments encrypt the masked cells and hash the masked cell using a corresponding hash. Embodiments store the hashed masked cell with a corresponding one or more identities of users who can use the hash to execute the masked cell.

Abstract: Embodiments implement a secure connector framework at a cloud infrastructure. Embodiments receive one or more notebook profiles from an on-premises system corresponding to a first cloud customer, the on-premises system comprising at least one of one or more datasets, one or more models, or one or more libraries, the notebook profiles comprising permission sets that specify a level of access to the datasets, the models and the libraries, the notebook profiles corresponding to an on-premises machine learning (“ML”) notebook. Embodiments transform the received notebook profiles into a cloud policy set for sharing the datasets, the models and the libraries. Embodiments then transmit and receive corresponding data from the first cloud customer to a second cloud customer, the transmitted and received data based on the cloud policy set.

Abstract: A model validation system is described that is configured to automatically validate model artifacts corresponding to models. For a model artifact being validated, the model validation system is configured to dynamically determine the validation checks to be performed for the model artifact, where the validation checks include various validation checks to be performed at the model artifact level and also for individual components included in the model artifact. The checks to be performed are dynamically determined based upon the attributes of the model artifact and of the components within the model artifact. The system is configured to generate a validation report that comprises information regarding the checks performed and the results generated from performing the various validation checks. The validation report may also include information suggesting actions for passing checks that result in a failed check, or for improving the scores of certain validation checks.