Model Share AI: An Integrated Toolkit for Collaborative Machine Learning
  Model Development, Provenance Tracking, and Deployment in Python
Heinrich Peters Michael Parrott
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.