Abstract: Various embodiments of the present technology include systems and methods for building, training, and executing new machine learning models via a no-code user environment. In some embodiments, a model definition is created by a user via a no-code machine learning model development toolkit and the model is queued for creation. A machine learning engine then implements processes described herein to build a machine learning model based on the model definition, train the model, automatically clean associated data for input into the model, and create a model instance to be stored in a model datastore. When new records are created or received, they may trigger the machine learning engine to run the model against the new record and provide valuable output.

Abstract: Various embodiments of the present technology generally relate to a constraint-based planning system. More specifically, some embodiments relate to a generic, scalable planning engine that may integrate into existing enterprise resource planning software. In one embodiment, a method of comprises initiating a planner engine on a computer according to a planner definition, wherein the planner definition identifies a planning problem and includes metadata identifying planning variables and constraints. The method further comprises gathering data from a database specified by the planner definition, parsing the data into planning data, and solving the planning problem based on the one or more constraints. The method further comprises identifying a final plan based on how well it satisfies the one or more constraints compared to at least one other plan and ultimately outputting the final plan.

Abstract: Various embodiments of the present technology generally relate to generating decisional data from organizational data. More specifically, some embodiments relate to aggregating and summarizing organizational data into cubes of decisional data in a no-code development environment. In one embodiment of the present technology, a method of providing decisional data to endpoints comprises aggregating organizational data into a cube of decisional data, wherein the cube is a multidimensional dataset comprising the decisional data. The method further comprises receiving a cube query from an endpoint, wherein the cube query is related to at least a portion of the decisional data in the cube and comprises metadata related to the cube, generating cube query results, wherein the cube query results comprise a summarized portion of the decisional data in the cube, and returning the cube query results to the endpoint.