Abstract: The present embodiments relate to data processing model recommendation and review of a portion of data using a recommended model. A model catalog executing on a cloud infrastructure (CI) system can parse data from an obtained dataset identifying aspects of the dataset. The parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. Review output data can be generated using the dataset and any of the recommended models. The review output data resulting from the recommended model can be provided to the client for the client to either accept or reject the model.

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 detect data drift associated with machine learning (“ML”) models. Embodiments identify a first feature stored by a feature store, where the feature store includes an offline store and an online store. Embodiments determine one or more first trained ML models that are using the first feature. For each of the first trained ML models, embodiments invoke the first trained ML model using synthetic data or validation data, generate metrics to determine an accuracy of the first trained ML model and, when the accuracy is below a threshold, generate an alert notifying of a first data drift for the first trained ML model.

Abstract: The present embodiments relate to data processing model recommendation and simulation. A model catalog executing on a cloud infrastructure (CI) system can parse data from an obtained dataset identifying aspects of the dataset. The parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. A simulation of any of the recommended models can be executed using a portion of the dataset to provide insights into output data resulting from executing the recommended model. The output data resulting from the simulation of the recommended model can be provided to the client for the client to either accept or reject the model.

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: Embodiments map a source schema to a target schema using a feature store. Embodiments receive a file including a plurality of source schema elements and a plurality of target schema elements, the file including a plurality of unmapped elements. Embodiments retrieve rule based mappings for the unmapped elements between the source schema elements and the target schema elements. Based on semantic matching of the source schema elements, embodiments retrieve feature store based mappings from the feature store for the unmapped elements between the source schema elements and the target schema elements. Embodiments then generate one or more similarity scores for mappings of the source schema elements to the target schema elements.

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: A data catalog system that is configured to automatically generate synthetic datasets based upon original datasets cataloged by the data catalog system, wherein each synthetic dataset comprises synthetic data that is generated using one or more data generation techniques. The data catalog system may access an original dataset and harvest associated metadata information and generate catalog information for the original dataset. The data catalog system may then generate a synthetic dataset based upon the original dataset and its harvested metadata information. The data catalog system may also generate catalog information for the generated synthetic dataset. The catalog information generated for the original dataset may be updated to refer to the newly generated synthetic dataset and its catalog information. The catalog information generated for the synthetic dataset may include references to the original dataset and its catalog information to inform a user of the original dataset about the synthetic dataset.

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: Embodiments operate a machine learning (“ML”) notebook in a cloud infrastructure executing a plurality of ML notebooks. Embodiments receive a plurality of previously executed ML notebook feature engineering commands from the plurality of ML notebooks. Embodiments store the plurality of previously executed ML notebook feature engineering commands, including a relationship between the feature engineering commands. Embodiments mine the stored commands to generate feature engineering sets of feature engineering commands, the feature engineering sets comprising feature engineering commands that are frequently used together and an order of use of the feature engineering commands. Embodiments then receive a context of a current feature engineering command and data used in the context and recommend a next feature engineering command to be executed after the current feature engineering command.

Abstract: Embodiments monitor for faults in a cloud based network for a plurality of features comprising an application and dependent features. Embodiments generate a graphical representation of the plurality of features comprising a plurality of nodes and corresponding relationships between the nodes, each node corresponding to one of the plurality of features. Embodiments monitor for events for the plurality of features, the events corresponding to one or more of the nodes, to generate monitored events. Embodiments populate a graph database with the monitored events and classify each of the nodes with a trained graph neural network (“GNN”), the classification comprising a prediction of a failure of at least one node. Based on the classifying, for a failure node corresponding to the prediction, embodiments generate a new alert for the failure node or revise a threshold for an existing alert for the failure node.

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 system is disclosed that is configured to perform various bias checks on an machine learning (ML) model in order to identify one or more biases, if any, that may be inherent to the ML model. Bias evaluation results generated from performing the checks are then reported to a user, such as to a consumer of the ML model, a data scientist responsible for modeling and training the ML model, and others. The bias evaluation system performs one or more bias checks by generating synthetic datasets using attributes present in the ML model or a training dataset used to train the ML model. Prediction data is then generated by inputting the synthetically generated input data points of the synthetic datasets into the ML model. The prediction data is then processed and evaluated for biases. Results of the evaluation may be compiled into a bias evaluation report.

Abstract: A framework for deploying, within a notebook session, a machine-learning model to an emulation environment. Responsive to a first input entered in a notebook requesting an emulator for a device: receiving, by a computer system, a first request for the emulator for the device, and identifying a compute instance that is loaded with the emulator for the device. Responsive to a second input entered in the notebook identifying an application package to be loaded in the compute instance, loading, by the computer system, the application package in the compute instance, and executing the emulator for the device based on the application package.

Abstract: A drift analysis system (DAS) is described that is capable of automatically detecting potential model schema drift issues when a machine learning model (MIL model), which has been trained using a particular training dataset, is used to make a prediction for a particular input provided to the model. The DAS performs one or more drift checks by comparing characteristics of the input to characteristics of the training dataset that was used to train the model that is being used to make a prediction for the input. Results obtained by the DAS from performing the drift checks may then be output along with the prediction made for the particular input. The one or more drift check results may be compiled into a drift report, which may be served concurrently with prediction results generated by the trained machine-learning model for the input.

Abstract: A data catalog system that is configured to automatically generate synthetic datasets based upon original datasets cataloged by the data catalog system, wherein each synthetic dataset comprises synthetic data that is generated using one or more data generation techniques. The data catalog system may access an original dataset and harvest associated metadata information and generate catalog information for the original dataset. The data catalog system may then generate a synthetic dataset based upon the original dataset and its harvested metadata information. The data catalog system may also generate catalog information for the generated synthetic dataset. The catalog information generated for the original dataset may be updated to refer to the newly generated synthetic dataset and its catalog information. The catalog information generated for the synthetic dataset may include references to the original dataset and its catalog information to inform a user of the original dataset about the synthetic dataset.

Abstract: The present embodiments relate to data processing model recommendation and simulation. A model catalog executing on a cloud infrastructure (CI) system can parse data from an obtained dataset identifying aspects of the dataset. The parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. A simulation of any of the recommended models can be executed using a portion of the dataset to provide insights into output data resulting from executing the recommended model. The output data resulting from the simulation of the recommended model can be provided to the client for the client to either accept or reject the model.

Abstract: Methods and apparatus are provided for managing heterogeneous enterprise software applications (apps). A plurality of enterprise applications are provided to users of an enterprise by providing an enterprise application store having a plurality of enterprise applications that are available for download to mobile devices of the users; downloading one or more of the enterprise applications to a mobile device of at least one of the users; and providing a common interface for a plurality of the enterprise applications to a plurality of back-end servers of the enterprise. The enterprise application store may comprise a plurality of layers.