Cornerstone-OnDemand/modelkit
Toolkit for developing and maintaining ML models
This is a Python framework designed for machine learning engineers to streamline the process of taking trained ML models from development to a production environment. It helps package your prediction code and associated configurations into a robust, deployable unit, ensuring the same logic runs consistently across different stages. It allows ML engineers to efficiently deploy models as services or within data processing pipelines.
151 stars. No commits in the last 6 months. Available on PyPI.
Use this if you are an ML engineer struggling with the complexities of deploying and maintaining machine learning models in production, especially when consistency, speed, and testability are crucial.
Not ideal if you are looking for a tool to train or build your machine learning models from scratch, as this framework focuses on the deployment and serving aspects.
Stars
151
Forks
16
Language
Python
License
MIT
Category
Last pushed
Jun 06, 2024
Commits (30d)
0
Dependencies
17
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