Abstract: The solution enables a data block composed of unstructured data assets (e.g., a Spark Dataframe) in a compute and analytics environment (e.g., Databricks) to be sent to a Training Data Platform (e.g., Labelbox, Inc.) for labeling. Then, the annotated structured dataset can be loaded back as a structured data asset (e.g., a Spark Dataframe) in the compute and analytics environment (e.g., Databricks). The solution provides an interface between a compute and analytics environment like Databricks and a Training Data Platform like Labelbox. The solution provides a Python library which facilitates data flow between the compute and analytics environment (e.g. Databricks) and the Training Data Platform (e.g., Labelbox). The library provides methods that help process the JSON annotations coming back from the training data platform.