User Guide

Our guides describe the major concepts in MLEM and how it works comprehensively, explaining when and how to use its features.

Codification: the MLEM way

Saving machine learning models to files or loading them back into Python objects may seem like a simple task at first. For example, the pickle and torch libraries can serialize/deserialize model objects to/from files. However, MLEM adds some "special sauce" by inspecting the objects and saving their metadata into .mlem metafiles and using these intelligently later on.

The metadata in .mlem files is necessary to reliably enable actions like packaging and serving different model types in various ways. MLEM allows us to automate a lot of the pain points we would hit in typical ML workflows by codifying and managing the information about our ML models (or other objects) for us.