Abstract: The disclosed technology is generally directed to the conversion of text to tables. In one example of the technology, input text that includes at least three rows is received. A plurality of characteristics of the input text is determined. Each characteristic of the plurality of characteristics is associated with a uniformity between the rows of the input text. The plurality of characteristics includes at least one characteristic that is associated with a delimiter count. A determination is made as to whether the input text is suitable for conversion to table based on the plurality of characteristics. Upon determining that the input text is suitable for conversion to a table, a machine learning model is used to convert the input text into a table.

Abstract: Solutions for optimizing job runtimes via prediction-based token allocation includes receiving training data comprising historical run data, the historical run data comprising job characteristics, runtime results, and a token count for each of a plurality of prior jobs, and the job characteristics comprising an intermediate representation and job graph data; based at least on the training data, training a token estimator, the token estimator comprising a machine learning (ML) model; receiving job characteristics for a user-submitted job; based at least on the received job characteristics, generating, with the token estimator, token prediction data for the user-submitted job; selecting a token count for the user-submitted job, based at least on the token prediction data; identifying the selected token count to an execution environment; and executing, with the execution environment, the user-submitted job in accordance with the selected token count.