Abstract: A parser is deployed early in a machine learning pipeline to read raw data and collect useful statistics about the raw data's content to determine which items of raw data exhibit a proxy for feature importance for the machine learning model. The parser operates at high speeds that approach the disk's absolute throughput while utilizing a small memory footprint. Utilization of the parser enables the machine learning pipeline to receive a fraction of the total raw data that would otherwise be available. Several scans through the data are performed, by which proxies for feature importance are indicated and irrelevant features may be discarded and thereby not forwarded to the machine learning pipeline. This reduces the amount of memory and other hardware resources used at the server and also expedites the machine learning process.

Abstract: A parser is deployed early in a machine learning pipeline to read raw data and collect useful statistics about the raw data's content to determine which items of raw data exhibit a proxy for feature importance for the machine learning model. The parser operates at high speeds that approach the disk's absolute throughput while utilizing a small memory footprint. Utilization of the parser enables the machine learning pipeline to receive a fraction of the total raw data that would otherwise be available. Several scans through the data are performed, by which proxies for feature importance are indicated and irrelevant features may be discarded and thereby not forwarded to the machine learning pipeline. This reduces the amount of memory and other hardware resources used at the server and also expedites the machine learning process.

Abstract: A parser is deployed early in a machine learning pipeline to read raw data and collect useful statistics about the raw data's content to determine which items of raw data exhibit a proxy for feature importance for the machine learning model. The parser operates at high speeds that approach the disk's absolute throughput while utilizing a small memory footprint. Utilization of the parser enables the machine learning pipeline to receive a fraction of the total raw data that would otherwise be available. Several scans through the data are performed, by which proxies for feature importance are indicated and irrelevant features may be discarded and thereby not forwarded to the machine learning pipeline. This reduces the amount of memory and other hardware resources used at the server and also expedites the machine learning process.