cid 067cb001989ecc2556318bb18d8eb290add5851f9a973cf9a27cd7cddadfa51e · original · via openalex
Title: Developments in MLflow Authors: Andrew Chen; Andy Chow; Aaron Davidson; Arjun D'Cunha; Ali Ghodsi; Sue Ann Hong; Andy Konwinski; Clemens Mewald; Siddharth Murching; Tomas Nykodym; Paul Ogilvie; Mani Parkhe; Avesh Kumar Singh; Fen Xie; Matei Zaharia; Richard Zang; Juntai Zheng; Corey Zumar Venue: Year: 2020 Canonical: doi:10.1145/3399579.3399867 URL: https://doi.org/10.1145/3399579.3399867 Collector: openalex OA: false ABSTRACT MLflow is a popular open source platform for managing ML development, including experiment tracking, reproducibility, and deployment. In this paper, we discuss user feedback collected since MLflow was launched in 2018, as well as three major features we have introduced in response to this feedback: a Model Registry for collaborative model management and review, tools for simplifying ML code instrumentation, and experiment analytics functions for extracting insights from millions of ML experiments.