Abstract: Technologies are provided for determining a suitability of data payloads for training a machine learning model. A schema can be generated based on sample data payloads that have different data formats. The sample data payloads (and/or additional data payloads) can be converted to a format that conforms to the schema. Feature vectors can then be generated based on the converted data payloads, and used to determine a suitability of the data payloads for training a machine learning model. If the data payloads are sufficiently suitable, the converted data payloads can be used to train the machine learning mode. Otherwise, the schema may be annotated and new converted payloads may be generated based on the annotated schema. The feature vector generation and suitability analysis can then be repeated.

Abstract: According to some embodiments, systems and methods are provided, comprising: receiving, at a user interface, a plurality of operator blocks and at least one connector for connecting the operator blocks to generate a dataflow model, wherein each connector includes a first endpoint and a second endpoint; receiving an annotation file for each operator, wherein the annotation file is received when the operator is received at the user interface; receiving at the user interface a positioning of each connector to connect two operator blocks; and generating a layout of the dataflow model on the user interface based on one or more semantic rules and one or more geometric rules. Numerous other aspects are provided.