Abstract: Techniques configuring a machine learning model include instantiating a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform. A selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model are received and sent to the machine learning model. A selection of one or more directories and an instruction to apply the machine learning model are sent to the machine learning model.

Abstract: Techniques configuring a machine learning model include receiving, via a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform, a selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model, which are sent to the machine learning model. As new files are uploaded to the selected directories of the collaborative computing platform, the machine learning model is applied to the uploaded files to classify the files and extract metadata. The extracted metadata and associated classification data are stored in data structures associated with the new files. The data structures are existing data structures of the collaborative computing platform.

Abstract: Techniques configuring a machine learning model include instantiating a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform. A selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model are received and sent to the machine learning model. A selection of one or more directories and an instruction to apply the machine learning model are sent to the machine learning model.

Abstract: Techniques configuring a machine learning model include receiving, via a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform, a selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model, which are sent to the machine learning model. As new files are uploaded to the selected directories of the collaborative computing platform, the machine learning model is applied to the uploaded files to classify the files and extract metadata. The extracted metadata and associated classification data are stored in data structures associated with the new files. The data structures are existing data structures of the collaborative computing platform.

Abstract: Techniques for asynchronous site provisioning in distributed computing systems are disclosed herein. In one embodiment, a method can include receiving a user request to initiate a provisioning process for a site. The user request can contain one or more specified features or functionalities for the site. The method also includes in response to the received user request from the user, performing provisioning of the site without provisioning for at least one of the specified features in the user request and providing the provisioned site for access by the user upon completion of performing provisioning of the site. The method further includes continuing provisioning the at least one of the specified features included in the user request while allowing the user to access the provided site without the at least one of the specified features via the computer network.

Abstract: Techniques for database selection during site provisioning in distributed computing systems are disclosed herein. In one embodiment, a method can include receiving a user request for a site to be hosted on one or more nodes in the distributed computing system. In response to the received user request, one of the databases is selected by randomly selecting a set of databases from a list of available databases, determining a subset of databases each having a lower level of database load than the other databases from the set of databases, and randomly selecting one database from the subset of databases. The method further includes storing a configuration file for the site in the randomly selected one database to enable the site to be accessible, the configuration file containing data representing one or more of module loading control, security configuration, session state configuration, application language, or compilation setting of the site.

Abstract: Techniques for work item management in distributed computing systems are disclosed herein. In one embodiment, a method can include receiving a user request from a user to initiate a provisioning process for a site to be hosted in the distributed computing system. In response to the received user request from the user, a work item containing one or more tasks to be performed in the provisioning process can be generated. The generated work item can then be enqueued in a work item queue with a future time that is later than a current time at which the generated work item is enqueued. Subsequently, the enqueued work item can be dequeued from the work item queue at a time earlier than the future time of the enqueued task to trigger performance of the one or more tasks contained in the work item related to the provisioning process of the site.

Abstract: Techniques for database selection during site provisioning in distributed computing systems are disclosed herein. In one embodiment, a method can include receiving a user request for a site to be hosted on one or more nodes in the distributed computing system. In response to the received user request, one of the databases is selected by randomly selecting a set of databases from a list of available databases, determining a subset of databases each having a lower level of database load than the other databases from the set of databases, and randomly selecting one database from the subset of databases. The method further includes storing a configuration file for the site in the randomly selected one database to enable the site to be accessible, the configuration file containing data representing one or more of module loading control, security configuration, session state configuration, application language, or compilation setting of the site.

Abstract: Techniques for asynchronous site provisioning in distributed computing systems are disclosed herein. In one embodiment, a method can include receiving a user request to initiate a provisioning process for a site. The user request can contain one or more specified features or functionalities for the site. The method also includes in response to the received user request from the user, performing provisioning of the site without provisioning for at least one of the specified features in the user request and providing the provisioned site for access by the user upon completion of performing provisioning of the site. The method further includes continuing provisioning the at least one of the specified features included in the user request while allowing the user to access the provided site without the at least one of the specified features via the computer network.

Abstract: A system and method for implementing a metadata search interface is disclosed. The technology creates a configuration file for the metadata search interface comprising a predicate describing a search parameter of the metadata search interface. The configuration file further comprises a control attribute which associates the predicate with a control describing how a display element displayed on the metadata search interface is displayed and a correlation between the display element and the predicate. The technology further comprises generating the metadata search interface in runtime based upon the control.

Abstract: A system and method for implementing a metadata search interface is disclosed. The technology creates a configuration file for the metadata search interface comprising a predicate describing a search parameter of the metadata search interface. The configuration file further comprises a control attribute which associates the predicate with a control describing how a display element displayed on the metadata search interface is displayed and a correlation between the display element and the predicate. The technology further comprises generating the metadata search interface in runtime based upon the control.

Abstract: A medical device may include a vascular access device with an access port which may include a septum and a slit. The slit may be formed on the inner surface of the body of the septum and the access port may be capable of receiving a separate access device through the slit of the septum. The medical device may also include a flexible member which expands to create an additional volume within the access port when the port is accessed by the access device. A method of controlling volume displacement a chamber of a medical device may include decreasing the volume of a chamber of an extravascular system by inserting a substance having a mass into the chamber and/or increasing the volume of the chamber simultaneously and commensurately with the mass of the substance inserted into the chamber.