Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python

• URL: http://arxiv.org/abs/2309.15719v1
• Date: Wed, 27 Sep 2023 15:24:39 GMT
• Title: Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python
• Authors: Heinrich Peters and Michael Parrott
• Abstract summary: We introduce Model Share AI (AIMS), an easy-to-use MLOps platform designed to streamline collaborative model development, model provenance tracking, and model deployment. AIMS features collaborative project spaces and a standardized model evaluation process that ranks model submissions based on their performance on unseen evaluation data. AIMS allows users to deploy ML models built in Scikit-Learn, Keras, PyTorch, and ONNX into live REST APIs and automatically generated web apps.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning (ML) has the potential to revolutionize a wide range of research areas and industries, but many ML projects never progress past the proof-of-concept stage. To address this issue, we introduce Model Share AI (AIMS), an easy-to-use MLOps platform designed to streamline collaborative model development, model provenance tracking, and model deployment, as well as a host of other functions aiming to maximize the real-world impact of ML research. AIMS features collaborative project spaces and a standardized model evaluation process that ranks model submissions based on their performance on unseen evaluation data, enabling collaborative model development and crowd-sourcing. Model performance and various model metadata are automatically captured to facilitate provenance tracking and allow users to learn from and build on previous submissions. Additionally, AIMS allows users to deploy ML models built in Scikit-Learn, TensorFlow Keras, PyTorch, and ONNX into live REST APIs and automatically generated web apps with minimal code. The ability to deploy models with minimal effort and to make them accessible to non-technical end-users through web apps has the potential to make ML research more applicable to real-world challenges.

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