Systems and methods for intelligent data compression

A method for intelligent data compression, the method comprising:

• receiving a first plurality of files for storage in a database;
• identifying data points in each of the first plurality of files;
• adding the identified data points to the database;
• identifying features in each of the first plurality of files;
• storing, for each of the first plurality of files, the identified features in the database as a surrogate for the respective file;
• receiving a second plurality of files for storage in the database;
• and
• iteratively, for each file of the second plurality of files and until each file of the second plurality of files is represented in the database, compress a respective file into a surrogate to reduce an amount of memory resources required to store the data of the respective file in the database by:
• identifying, using a first machine learning model, one or more data points in the respective file, wherein each identified data point was previously unidentified with respect to a stored file in the database;
• adding the identified one or more data points to the database;
• identifying, using a second machine learning model, one or more features of the respective file for storage in the database;
• and
• storing the identified one or more features in the database as the surrogate for the respective file.