1. MLEM can package an ML model into a Docker image or a Python package, and deploy it to, for example, Heroku.

2. MLEM saves all model metadata to a human-readable text file: Python environment, model methods, model input & output data schema and more.

3. MLEM helps you turn your Git repository into a Model Registry with features like ML model lifecycle management.

Our philosophy is that MLOps tools should be built using the Unix approach - each tool solves a single problem, but solves it very well. MLEM was designed to work hands on hands with Git - it saves all model metadata to a human-readable text files and Git becomes a source of truth for ML models. Model weights file can be stored in the cloud storage using a Data Version Control tool or such - independently of MLEM.

Please check out the project: https://github.com/iterative/mlem and the website: https://mlem.ai

I’d love to hear your feedback!