Abstract: A document execution engine receives a training set of data including training documents that each include one or more passages associated with a passage type and a level of risk. The document execution engine trains a machine learned model based on the training set. The trained machine learned model, when applied to subsequently identified passages within documents in the document execution environment, can identify a passage with above threshold levels of risk (e.g., a high-risk passage) based on a passage type of the passage. The trained machine learned model can then provide for display the high-risk passage and a related passage of the same passage type from a second document within the document execution environment to the user via a document passage comparison interface. Differences between the passages can be highlighted, enabling a user to quickly compare and contrast the passages.

Abstract: A document execution engine receives a training set of data including training documents that each include one or more passages associated with a passage type and a level of risk. The document execution engine trains a machine learned model based on the training set. The trained machine learned model, when applied to subsequently identified passages within documents in the document execution environment, can identify a passage with above threshold levels of risk (e.g., a high-risk passage) based on a passage type of the passage. The trained machine learned model can then provide for display the high-risk passage and a related passage of the same passage type from a second document within the document execution environment to the user via a document passage comparison interface. Differences between the passages can be highlighted, enabling a user to quickly compare and contrast the passages.

Abstract: A particular method includes storing a manifest file and a parameters file at a data store. The manifest file and the parameters file are associated with a software deployment package. An application associated with the software deployment package is published to a first remote computing device and to a second remote computing device. The method includes receiving first parameter values from the first remote computing device and receiving second parameter values from the second remote computing device. The first parameter values are stored in a first region of the data store that is specific to the first computing device. The second parameter values are stored in a second region of the data store that is specific to the second computing device.

Abstract: A particular method includes storing a manifest file and a parameters file at a data store. The manifest file and the parameters file are associated with a software deployment package. An application associated with the software deployment package is published to a first remote computing device and to a second remote computing device. The method includes receiving first parameter values from the first remote computing device and receiving second parameter values from the second remote computing device. The first parameter values are stored in a first region of the data store that is specific to the first computing device. The second parameter values are stored in a second region of the data store that is specific to the second computing device.

Abstract: A document execution engine receives a training set of data including training documents that each include one or more passages associated with a passage type and a level of risk. The document execution engine trains a machine learned model based on the training set. The trained machine learned model, when applied to subsequently identified passages within documents in the document execution environment, can identify a passage with above threshold levels of risk (e.g., a high-risk passage) based on a passage type of the passage. The trained machine learned model can then provide for display the high-risk passage and a related passage of the same passage type from a second document within the document execution environment to the user via a document passage comparison interface. Differences between the passages can be highlighted, enabling a user to quickly compare and contrast the passages.

Abstract: A document execution engine receives a training set of data including training documents that each include one or more passages associated with a passage type and a level of risk. The document execution engine trains a machine learned model based on the training set. The trained machine learned model, when applied to subsequently identified passages within documents in the document execution environment, can identify a passage with above threshold levels of risk (e.g., a high-risk passage) based on a passage type of the passage. The trained machine learned model can then provide for display the high-risk passage and a related passage of the same passage type from a second document within the document execution environment to the user via a document passage comparison interface. Differences between the passages can be highlighted, enabling a user to quickly compare and contrast the passages.

Abstract: A document execution engine receives a training set of data including training documents that each include one or more passages associated with a passage type and a level of risk. The document execution engine trains a machine learned model based on the training set. The trained machine learned model, when applied to subsequently identified passages within documents in the document execution environment, can identify a passage with above threshold levels of risk (e.g., a high-risk passage) based on a passage type of the passage. The trained machine learned model can then provide for display the high-risk passage and a related passage of the same passage type from a second document within the document execution environment to the user via a document passage comparison interface. Differences between the passages can be highlighted, enabling a user to quickly compare and contrast the passages.

Abstract: A particular method includes storing a manifest file and a parameters file at a data store. The manifest file and the parameters file are associated with a software deployment package. An application associated with the software deployment package is published to a first remote computing device and to a second remote computing device. The method includes receiving first parameter values from the first remote computing device and receiving second parameter values from the second remote computing device. The first parameter values are stored in a first region of the data store that is specific to the first computing device. The second parameter values are stored in a second region of the data store that is specific to the second computing device.

Abstract: A particular method includes storing a manifest file and a parameters file at a data store. The manifest file and the parameters file are associated with a software deployment package. An application associated with the software deployment package is published to a first remote computing device and to a second remote computing device. The method includes receiving first parameter values from the first remote computing device and receiving second parameter values from the second remote computing device. The first parameter values are stored in a first region of the data store that is specific to the first computing device. The second parameter values are stored in a second region of the data store that is specific to the second computing device.

Abstract: Methods, systems, and computer program products are provided that enable web applications to be identified, and that enable the identified web applications to be published to remote servers. Objects are accessed in storage for a developed web application. A feed is received that includes a plurality of signatures that correspond to a plurality of webapp development applications. A webapp development application used to develop the web application is identified by comparing the signatures to the objects. A web deployment package is downloaded for the identified webapp development application. The downloaded web deployment package is applied to enable the web application to be published to a server, such as by modifying settings and/or applying transformations to the objects that defined by the downloaded web deployment package.

Abstract: A method of provisioning a Web hosting resource includes providing a cloud service. A request for a Web hosting resource is received by the cloud service, wherein the request is provided by a client. The cloud service identifies a Web host based on the received request for a Web hosting resource. The cloud service sends a request to the Web host to provision a first Web hosting resource for use by the client.

Abstract: Methods, systems, and computer program products are provided that enable web applications to be identified, and that enable the identified web applications to be published to remote servers. Objects are accessed in storage for a developed web application. A feed is received that includes a plurality of signatures that correspond to a plurality of webapp development applications. A webapp development application used to develop the web application is identified by comparing the signatures to the objects. A web deployment package is downloaded for the identified webapp development application. The downloaded web deployment package is applied to enable the web application to be published to a server, such as by modifying settings and/or applying transformations to the objects that defined by the downloaded web deployment package.

Abstract: Methods, systems, and computer program products are provided that enable web applications to be identified, and that enable the identified web applications to be published to remote servers. Objects are accessed in storage for a developed web application. A feed is received that includes a plurality of signatures that correspond to a plurality of webapp development applications. A webapp development application used to develop the web application is identified by comparing the signatures to the objects. A web deployment package is downloaded for the identified webapp development application. The downloaded web deployment package is applied to enable the web application to be published to a server, such as by modifying settings and/or applying transformations to the objects that defined by the downloaded web deployment package.

Abstract: A method of provisioning a Web hosting resource includes providing a cloud service. A request for a Web hosting resource is received by the cloud service, wherein the request is provided by a client. The cloud service identifies a Web host based on the received request for a Web hosting resource. The cloud service sends a request to the Web host to provision a first Web hosting resource for use by the client.

Abstract: A method of provisioning a Web hosting resource includes providing a cloud service. A request for a Web hosting resource is received by the cloud service, wherein the request is provided by a client. The cloud service identifies a Web host based on the received request for a Web hosting resource. The cloud service sends a request to the Web host to provision a first Web hosting resource for use by the client.