mlem 0.1.14

Version and deploy your models following GitOps principles

MLEM helps you with model deployment. It saves ML models in a standard format that can be used in a variety of downstream deployment scenarios such as real-time serving through a REST API or batch processing. MLEM format is a human-readable text that helps you use GitOps with Git as the single source of truth.

• Run your model anywhere you want: package it as a Python package, a Docker Image or deploy it to Heroku (SageMaker, Kubernetes and more platforms are coming). Switch between formats and deployment platforms with a single command thanks to unified abstraction.
• Simple text file to save model metadata: automatically package Python env requirements and input data specifications into a ready-to-deploy format. Use the same human-readable format for any ML framework.
• Stick to your training workflow: MLEM doesn't ask you to rewrite your training code. To start using packaging or deployment machinery, add just two lines to your python script: one to import the library and one to save the model.
• Developer-first experience: use CLI when you feel like DevOps and API when you feel like a developer.

Why MLEM?

The main reason to use MLEM instead of other related solutions is that it works well with GitOps approach and helps you manage model lifecycle in Git:

• Git as a single source of truth: we use plain text to save metadata for models that can be saved and versioned.
• Reuse existing Git and Github/Gitlab infrastructure for model management instead of installing separate model management software.
• Unify model and software deployment. Deploy models using the same processes and code you use to deploy software.

Installation

Install MLEM with pip:

$ pip install mlem

To install the development version, run:

$ pip install git+https://github.com/iterative/mlem