Abstract: Various embodiments include a classification platform system. A user can define a classification experiment on the classification platform system. For example, the user can define an input data space by selecting at least one of data sources interfaced with the classification platform system and defining a workflow configuration including a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow. The DG can specify how one or more outputs of each of the transformation blocks are fed into one or more other transformation blocks. The DG can be executed by various types of computation platforms. The classification platform system can schedule the experiment workflow to be executed on a distributed computation platform according to the input data space and the workflow configuration.

Abstract: Various embodiments include a classification platform system. A user can define a classification experiment on the classification platform system. For example, the user can define an input data space by selecting at least one of data sources interfaced with the classification platform system and defining a workflow configuration including a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow. The DG can specify how one or more outputs of each of the transformation blocks are fed into one or more other transformation blocks. The DG can be executed by various types of computation platforms. The classification platform system can schedule the experiment workflow to be executed on a distributed computation platform according to the input data space and the workflow configuration.

Abstract: An online system ranks topic-groups for users and presents content items in topic-based feeds. A topic group corresponds to one or more topic(s) and can be used to generate a feed for presenting the content items related to the topic(s). For a particular user, the topic groups are ranked according to the likelihood of the user interacting with content items included in the topic groups. The topic groups are ranked using information of the users and/or users' historical interaction data such as click-based interaction data, post-based interaction data, or engagement-based interaction data. The online system generates and provides a user interface for presenting the topic groups to the client device. Content items that are related to the topic(s) corresponding to the topic group are presented in each topic-based feed such that the user can switch between different topic-based feeds.

Abstract: In one embodiment, a method includes defining an input data space, defining a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow, wherein at least one of the plurality of transformation blocks includes logic to dynamically modify the DG during execution of the experiment workflow, formatting the DG and the input data space into a data structure such that the data structure is interpretable by a plurality of different computation platforms, scheduling a distributed computation platform selected from the plurality of different computation platforms to execute the experiment workflow according to the input data space and the DG, and imperatively programming computing nodes of the distributed computation platform to execute the experiment workflow based on the DG if at least one of the transformation blocks dynamically modifies the DG during the execution of the experiment workflow.

Abstract: Various embodiments include a classification platform system. A user can define a classification experiment on the classification platform system. For example, the user can define an input data space by selecting at least one of data sources interfaced with the classification platform system and defining a workflow configuration including a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow. The DG can specify how one or more outputs of each of the transformation blocks are fed into one or more other transformation blocks. The DG can be defined graphically. The classification platform system can schedule the experiment workflow to be executed on a distributed computation platform according to the input data space and the workflow configuration.

Abstract: An online system ranks topic-groups for users and presents content items in topic-based feeds. A topic group corresponds to one or more topic(s) and can be used to generate a feed for presenting the content items related to the topic(s). For a particular user, the topic groups are ranked according to the likelihood of the user interacting with content items included in the topic groups. The topic groups are ranked using information of the users and/or users' historical interaction data such as click-based interaction data, post-based interaction data, or engagement-based interaction data. The online system generates and provides a user interface for presenting the topic groups to the client device. Content items that are related to the topic(s) corresponding to the topic group are presented in each topic-based feed such that the user can switch between different topic-based feeds.

Abstract: Various embodiments include a classification platform system. A user can define a classification experiment on the classification platform system. For example, the user can define an input data space by selecting at least one of data sources interfaced with the classification platform system and defining a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow. The DG can specify how one or more outputs of each of the transformation blocks are fed into one or more other transformation blocks. At least one of the transformation block can dynamically modify the DG. The classification platform system can schedule the experiment workflow to be executed on a distributed computation platform according to the input data space and the DG.

Abstract: Various embodiments include a classification platform system. A user can define a classification experiment on the classification platform system. For example, the user can define an input data space by selecting at least one of data sources interfaced with the classification platform system and defining a workflow configuration including a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow. The DG can specify how one or more outputs of each of the transformation blocks are fed into one or more other transformation blocks. The DG can be executed by various types of computation platforms. The classification platform system can schedule the experiment workflow to be executed on a distributed computation platform according to the input data space and the workflow configuration.

Abstract: Various embodiments include a classification platform system. A user can define a classification experiment on the classification platform system. For example, the user can define an input data space by selecting at least one of data sources interfaced with the classification platform system and defining a workflow configuration including a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow. The DG can specify how one or more outputs of each of the transformation blocks are fed into one or more other transformation blocks. The DG can be defined graphically. The classification platform system can schedule the experiment workflow to be executed on a distributed computation platform according to the input data space and the workflow configuration.