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Machine learning (ML) has the potential to revolutionize a wide range ofresearch areas and industries, but many ML projects never progress past theproof-of-concept stage. To address this issue, we introduce Model Share AI(AIMS), an easy-to-use MLOps platform designed to streamline collaborativemodel development, model provenance tracking, and model deployment, as well asa host of other functions aiming to maximize the real-world impact of MLresearch. AIMS features collaborative project spaces and a standardized modelevaluation process that ranks model submissions based on their performance onunseen evaluation data, enabling collaborative model development andcrowd-sourcing. Model performance and various model metadata are automaticallycaptured to facilitate provenance tracking and allow users to learn from andbuild on previous submissions. Additionally, AIMS allows users to deploy MLmodels built in Scikit-Learn, TensorFlow Keras, PyTorch, and ONNX into liveREST APIs and automatically generated web apps with minimal code. The abilityto deploy models with minimal effort and to make them accessible tonon-technical end-users through web apps has the potential to make ML researchmore applicable to real-world challenges.

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