Abstract: A method for executing a machine learning model using a workflow engine includes receiving a model configuration including data related to the machine learning model, pre-processing steps having first prerequisites, and post-processing steps having second prerequisites; in response to a determination that the first prerequisites are not met, executing first operations; in response to a determination that the second prerequisites are not met, executing second operations; executing the pre-processing steps to provide first data, the first data including model inputs; causing transmission of the first data from the computer system to the cloud server system; causing execution of the machine learning model on the cloud server system; causing transmission of second data from the cloud server system to the computer system, the second data including an output of the machine learning model; executing the post-processing steps.

Abstract: Methods and apparatuses are described for capturing and maintaining a timeline of data changes in a relational database system. A server identifies changed records from relational database tables. The server analyzes the changed records to determine a maximum timestamp for each primary key and extracts the changed records associated with each primary key where a timestamp is equal to or greater than the maximum timestamp for the primary key. The server generates timestamp ranges for each primary key, each comprising an effective date and an expiration date. The server determines whether each key-date combination already exists in a historical record table. The server updates an expiration date of an existing record in the historical record table using the effective date and inserts a new record for the timestamp range using the captured records.