Open-source model registry and deployment tool for Machine Learning_

  • Codify your model into a standardized format

    Automatically extract environment, methods, and input data specification

  • Turn your Git repo into model registry

    Reuse existing Git and GitHub/GitLab infrastructure for model management

  • Use CLI to pack, dockerize and deploy

    Easily switch between different packaging formats and cloud providers

  • Use Python API to load and apply your models

    Load models dynamically from any storage or model registry

Become first user
  • Tensorflow logo
  • PyTorch logo
  • dmlc xgboost logo
  • scikit learn logo
  • Light GBM logo
  • Keras logo
  • Catboost logo

Why MLEM_

  • Unambiguous link between data, code, model and metrics
  • Standardised model packaging with environment and input data specification
  • Model lifecycle management using GitOps approach
  • Cloud-agnostic model deployment