Touchups

papers/heinrich_peters/main.tex

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-\title{Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python}
+\title[Model Share AI]{Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python}
 \begin{abstract}
 Machine learning (ML) is revolutionizing a wide range of research areas and industries, but many ML projects never progress past the proof-of-concept stage. To address this problem, we introduce Model Share AI (AIMS), a 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 holdout evaluation data, enabling users to run experiments and competitions. In addition, various model metadata are automatically captured to facilitate provenance tracking and allow users to learn from and build on previous submissions. Furthermore, AIMS allows users to deploy ML models built in Scikit-Learn, TensorFlow Keras, or PyTorch into live REST APIs and automatically generated web apps with minimal code. The ability to collaboratively develop and rapidly deploy models, making them accessible to non-technical end-users through automatically generated web apps, ensures that ML projects can transition smoothly from concept to real-world application.