Abstract: A model management system provides a centralized repository for storing and accessing models. The model management system receives an input to store a model object in a first model state generated based on a first set of known variables. The model management system generates a first file including a first set of functions defining the first model state and associates the first file with a model key identifying the model object. The model management system receives an input to store the model object in a second model state having been generated based on the first model state and a second set of known variables. The model management system generates a second file including a second set of functions defining the second model state and associates the second file with the model key. The model management system identifies available versions of the model object based on the model key.

Abstract: One or more virtual machines are launched at an application platform. At each of the one or more virtual machines, a machine learning model execution environment is instantiated for an instance of a machine learning model. A respective instance of the machine learning model is loaded to each machine learning model execution environment. Each loaded instance of the machine learning model is associated with an application programming interface (API) endpoint which can receive input data for the loaded instance of the machine learning model from a client device and return output data produced by the loaded instance of the machine learning model based on the input data.

Abstract: One or more virtual machines are launched at an application platform. At each of the one or more virtual machines, a machine learning model execution environment is instantiated for an instance of a machine learning model. A respective instance of the machine learning model is loaded to each machine learning model execution environment. Each loaded instance of the machine learning model is associated with an application programming interface (API) endpoint which can receive input data for the loaded instance of the machine learning model from a client device and return output data produced by the loaded instance of the machine learning model based on the input data.

Abstract: A model management system provides a centralized repository for storing and accessing models. The model management system receives an input to store a model object in a first model state generated based on a first set of known variables. The model management system generates a first file including a first set of functions defining the first model state and associates the first file with a model key identifying the model object. The model management system receives an input to store the model object in a second model state having been generated based on the first model state and a second set of known variables. The model management system generates a second file including a second set of functions defining the second model state and associates the second file with the model key. The model management system identifies available versions of the model object based on the model key.

Abstract: A model management system provides a centralized repository for storing and accessing models. The model management system receives an input to store a model object in a first model state generated based on a first set of known variables. The model management system generates a first file including a first set of functions defining the first model state and associates the first file with a model key identifying the model object. The model management system receives an input to store the model object in a second model state having been generated based on the first model state and a second set of known variables. The model management system generates a second file including a second set of functions defining the second model state and associates the second file with the model key. The model management system identifies available versions of the model object based on the model key.

Abstract: One or more virtual machines are launched at an application platform. At each of the one or more virtual machines, a machine learning model execution environment is instantiated for an instance of a machine learning model. A respective instance of the machine learning model is loaded to each machine learning model execution environment. Each loaded instance of the machine learning model is associated with an application programming interface (API) endpoint which can receive input data for the loaded instance of the machine learning model from a client device and return output data produced by the loaded instance of the machine learning model based on the input data.

Abstract: Systems and methods are validating data in a data set. A data set including data to validate and a validator to use in validating the data is selected based on user input generated based on interactions of a user with a graphical user interface. The validator is applied to the data to determine whether one or more statistics generated through application of the validator to the data is valid or invalid based on a validation routine associated with the validator. A data quality report indicating whether the data set is valid or invalid, based on a determination of whether the one or more statistics is valid or invalid, is generated and selectively presented to the user through the graphical user interface.

Abstract: A data analysis system presents a user interface to allow a user to provide a natural language query pertaining to a dataset, wherein the dataset is associated with a data object model comprising a plurality of objects and receives, via the user interface, user input specifying the natural language query. The data analysis system further modifies, in the user interface, the user input to visually indicate one or more portions of the natural language query that each represent one of the plurality of objects and presents, in the user interface, a response to the natural language query, the response being based on data from the dataset, the data corresponding to the one of the plurality of objects.

Abstract: One or more virtual machines are launched at an application platform. At each of the one or more virtual machines, a machine learning model execution environment is instantiated for an instance of a machine learning model. A respective instance of the machine learning model is loaded to each machine learning model execution environment. Each loaded instance of the machine learning model is associated with an application programming interface (API) endpoint which can receive input data for the loaded instance of the machine learning model from a client device and return output data produced by the loaded instance of the machine learning model based on the input data.

Abstract: A data analysis system receives a data string comprising a natural language query pertaining to a dataset, wherein the dataset is associated with a data object model comprising a plurality of objects, and parses the data string to identify a plurality of individual words within the data string. The data analysis system identifies, based on the plurality of individual words, one or more objects of the plurality of objects, wherein the one or more objects are associated with the natural language query in the data string. The data analysis system further determines one or more artifacts that are based on the dataset, wherein each of the one or more artifacts is associated with one of the one or more objects and provides a response to the natural language query.

Abstract: Systems and methods are validating data in a data set. A data set including data to validate and a validator to use in validating the data is selected based on user input generated based on interactions of a user with a graphical user interface. The validator is applied to the data to determine whether one or more statistics generated through application of the validator to the data is valid or invalid based on a validation routine associated with the validator. A data quality report indicating whether the data set is valid or invalid, based on a determination of whether the one or more statistics is valid or invalid, is generated and selectively presented to the user through the graphical user interface.

Abstract: A model management system provides a centralized repository for storing and accessing models. The model management system receives an input to store a model object in a first model state generated based on a first set of known variables. The model management system generates a first file including a first set of functions defining the first model state and associates the first file with a model key identifying the model object. The model management system receives an input to store the model object in a second model state having been generated based on the first model state and a second set of known variables. The model management system generates a second file including a second set of functions defining the second model state and associates the second file with the model key. The model management system identifies available versions of the model object based on the model key.

Abstract: One or more virtual machines are launched at an application platform. At each of the one or more virtual machines, a machine learning model execution environment is instantiated for an instance of a machine learning model. A respective instance of the machine learning model is loaded to each machine learning model execution environment. Each loaded instance of the machine learning model is associated with an application programming interface (API) endpoint which can receive input data for the loaded instance of the machine learning model from a client device and return output data produced by the loaded instance of the machine learning model based on the input data.

Abstract: A model management system provides a centralized repository for storing and accessing models. The model management system receives an input to store a model object in a first model state generated based on a first set of known variables. The model management system generates a first file including a first set of functions defining the first model state and associates the first file with a model key identifying the model object. The model management system receives an input to store the model object in a second model state having been generated based on the first model state and a second set of known variables. The model management system generates a second file including a second set of functions defining the second model state and associates the second file with the model key. The model management system identifies available versions of the model object based on the model key.

Abstract: Techniques for configuring and validating a data pipeline system deployment are described. In an embodiment, a template is a file or data object that describes a package of related jobs. For example, a template may describe a set of jobs necessary for deduplication of data records or a set of jobs performing machine learning on a set of data records. The template can be defined in a file, such as a JSON blob or XML file. For each job specified in the template, the template may identify a set of dataset dependencies that are needed as input for the processing of that job. For each job specified in the template, the template may further identify a set of configuration parameters needed for deployment of the job. In an embodiment, a server uses the template and the configuration parameter values collected via the GUI to generate code for the package of jobs. The code may be stored in a version control system. In an embodiment, the code may be compiled, executed, and deployed to a server for processing the data.

Abstract: Systems and methods are validating data in a data set. A data set including data to validate and a validator to use in validating the data is selected based on user input generated based on interactions of a user with a graphical user interface. The validator is applied to the data to determine whether one or more statistics generated through application of the validator to the data is valid or invalid based on a validation routine associated with the validator. A data quality report indicating whether the data set is valid or invalid, based on a determination of whether the one or more statistics is valid or invalid, is generated and selectively presented to the user through the graphical user interface.

Abstract: Techniques for configuring and validating a data pipeline system deployment are described. In an embodiment, a template is a file or data object that describes a package of related jobs. For example, a template may describe a set of jobs necessary for deduplication of data records or a set of jobs performing machine learning on a set of data records. The template can be defined in a file, such as a JSON blob or XML file. For each job specified in the template, the template may identify a set of dataset dependencies that are needed as input for the processing of that job. For each job specified in the template, the template may further identify a set of configuration parameters needed for deployment of the job. In an embodiment, a server uses the template and the configuration parameter values collected via the GUI to generate code for the package of jobs. The code may be stored in a version control system. In an embodiment, the code may be compiled, executed, and deployed to a server for processing the data.

Abstract: Systems and methods are validating data in a data set. A data set including data to validate and a validator to use in validating the data is selected based on user input generated based on interactions of a user with a graphical user interface. The validator is applied to the data to determine whether one or more statistics generated through application of the validator to the data is valid or invalid based on a validation routine associated with the validator. A data quality report indicating whether the data set is valid or invalid, based on a determination of whether the one or more statistics is valid or invalid, is generated and selectively presented to the user through the graphical user interface.

Abstract: Techniques for a classification system with methodology for enhanced verification are described. In one approach, a classification computer trains a classifier based on a set of training documents. After training is complete, the classification computer iterates over a collection unlabeled documents uses the trained classifier to predict a label for each unlabeled document. A verification computer retrieves one of the documents assigned a label by the classification computer. The verification computer then generates a user interface that displays select information from the document and provides an option to verify the label predicted by the classification computer or provide an alternative label. The document and the verified label are then fed back into the set of training documents and are used to retrain the classifier to improve subsequent classifications. In addition, the document is indexed by a query computer based on the verified label and made available for search and display.

Abstract: Techniques for a classification system with methodology for enhanced verification are described. In one approach, a classification computer trains a classifier based on a set of training documents. After training is complete, the classification computer iterates over a collection unlabeled documents uses the trained classifier to predict a label for each unlabeled document. A verification computer retrieves one of the documents assigned a label by the classification computer. The verification computer then generates a user interface that displays select information from the document and provides an option to verify the label predicted by the classification computer or provide an alternative label. The document and the verified label are then fed back into the set of training documents and are used to retrain the classifier to improve subsequent classifications. In addition, the document is indexed by a query computer based on the verified label and made available for search and display.