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

**Authors:** Heinrich Peters, Michael Parrott  
**arXiv:** [2309.15719](https://arxiv.org/abs/2309.15719)  
**Date:** 2023-09-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.15719v1/

## Key points

- Authors Heinrich Peters and Michael Parrott discuss the transformative potential of machine learning (ML) across research domains and industries
- Introduction of Model Share AI (AIMS) as a user-friendly MLOps platform to streamline collaborative model development, track model provenance, and facilitate model deployment
- AIMS offers collaborative project spaces and implements a standardized model evaluation process based on performance on unseen evaluation data
- Enables collaborative model development and crowd-sourcing efforts within the ML community
- Automatically captures key metrics related to model performance and metadata for provenance tracking
- Capability to deploy ML models created using popular frameworks into live REST APIs with minimal coding requirements
- Ability to generate web applications automatically for accessibility to non-technical end-users
- AIMS aims to bridge the gap between cutting-edge ML research and practical real-world challenges

## Summary

In their paper titled "Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python," authors Heinrich Peters and Michael Parrott discuss the transformative potential of machine learning (ML) across various research domains and industries. Despite this potential, many ML projects often stall at the proof-of-concept stage. To address this common issue, the authors introduce Model Share AI (AIMS), a user-friendly MLOps platform aimed at streamlining collaborative model development, tracking model provenance, and facilitating model deployment to maximize the real-world impact of ML research. AIMS offers collaborative project spaces and implements a standardized model evaluation process that assesses model submissions based on their performance on unseen evaluation data. This approach enables collaborative model development and crowd-sourcing efforts within the ML community. The platform also automatically captures key metrics related to model performance and metadata to support provenance tracking, allowing users to learn from previous submissions and build upon existing models effectively. One notable feature of AIMS is its capability to deploy ML models created using popular frameworks such as Scikit-Learn, TensorFlow Keras, PyTorch, and ONNX into live REST APIs with minimal coding requirements. Additionally, AIMS can generate web applications automatically to make these models accessible to non-technical end-users. By simplifying the deployment process and enhancing accessibility through web apps, AIMS aims to bridge the gap between cutting-edge ML research and practical real-world challenges. Overall, Model Share AI represents a significant advancement in the field of machine learning by providing researchers with a comprehensive toolkit for collaborative development, robust provenance tracking, seamless deployment capabilities, and enhanced accessibility for broader societal impact.