A method includes obtaining feature generation code from, which is configured to determine features relating to input data. The method further includes obtaining data grouping code, which is configured to generate training data by determining a plurality of data groupings for the features relating to the input data. The method further includes obtaining modeling code, which is derived at least in part by applying one or more machine learning algorithms to the training data. The method further includes applying a model wrapper code to the feature generation code, the data grouping code, and the modeling code to generate a model wrapper and deploying the model wrapper such that the model wrapper may receive a first application programming interface (API) call including an input data value, determine a score relating to the input data value, and send a second API call including the score in response to the first API call.

A method includes obtaining feature generation code from, which is configured to determine features relating to input data. The method further includes obtaining data grouping code, which is configured to generate training data by determining a plurality of data groupings for the features relating to the input data. The method further includes obtaining modeling code, which is derived at least in part by applying one or more machine learning algorithms to the training data. The method further includes applying a model wrapper code to the feature generation code, the data grouping code, and the modeling code to generate a model wrapper and deploying the model wrapper such that the model wrapper may receive a first application programming interface (API) call including an input data value, determine a score relating to the input data value, and send a second API call including the score in response to the first API call.

Managing content is disclosed. An indication is received that a content item comprising a body of managed content is associated with a business object not included in the body of managed content. The content item is linked with the business object.

Managing content is disclosed. An indication is received that a content item comprising a body of managed content is associated with a business object not included in the body of managed content. The content item is linked with the business object.

Documents, such as those that may or will be the subject of a litigation, may be managed by automatically determining that a document, such as an email or other communication, is privileged or producible such that superfluous documents may be removed to improve data storage and reduce the burden on storage, processing, and communication resources. Additionally, documents such as emails may comprise attached or embedded documents (e.g., attachments) which may be similarly or independently classified from their associated email. After determining privilege, such as via metadata associated with a sender/receiver of an email, similarly categorized documents may be grouped for presentation and/or storage. The documents may be indexed, such as by entries within a production log, to further facilitate accurate production and management of non-privileged documents, as well as, the exclusion of privileged documents. Documents not required for production may be indexed and/or purged from storage.

Documents, such as those that may or will be the subject of a litigation, may be managed by automatically determining that a document, such as an email or other communication, is privileged or producible such that superfluous documents may be removed to improve data storage and reduce the burden on storage, processing, and communication resources. Additionally, documents such as emails may comprise attached or embedded documents (e.g., attachments) which may be similarly or independently classified from their associated email. After determining privilege, such as via metadata associated with a sender/receiver of an email, similarly categorized documents may be grouped for presentation and/or storage. The documents may be indexed, such as by entries within a production log, to further facilitate accurate production and management of non-privileged documents, as well as, the exclusion of privileged documents. Documents not required for production may be indexed and/or purged from storage.

An unstructured data input is accessed that includes an electronic communication. Content of the unstructured data is parsed to determine one or more terms in the unstructured data input. It is determined that one or more particular elements defined in a structured business data model correspond to the terms. Tags are assigned to the unstructured data based on the terms corresponding to the one or more particular elements, where the tags define an association between the unstructured data and the structured data model.

An unstructured data input is accessed that includes an electronic communication. Content of the unstructured data is parsed to determine one or more terms in the unstructured data input. It is determined that one or more particular elements defined in a structured business data model correspond to the terms. Tags are assigned to the unstructured data based on the terms corresponding to the one or more particular elements, where the tags define an association between the unstructured data and the structured data model.

An in-context cognitive information assistant is provided by: obtaining a context for a user, wherein the context comprises a calendar activity with one or more other users; supplementing the context by obtaining one or more conversations with the user related to the context; extracting cognitive data for the context and the conversations; and finding relevant materials in a corpus using the cognitive data. The relevant materials are used to prepare the user for interactions with other users.

An in-context cognitive information assistant is provided by: obtaining a context for a user, wherein the context comprises a calendar activity with one or more other users; supplementing the context by obtaining one or more conversations with the user related to the context; extracting cognitive data for the context and the conversations; and finding relevant materials in a corpus using the cognitive data. The relevant materials are used to prepare the user for interactions with other users.