Accelerating the Machine Learning Lifecycle with MLflow. Zaharia, M., Chen, A., Davidson, A., Ghodsi, A., Hong, S. A., Konwinski, A., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Xie, F., & Zumar, C. IEEE Data Engineering Bulletin, 41(4):7, December, 2018. abstract bibtex Machine learning development creates multiple new challenges that are not present in a traditional software development lifecycle. These include keeping track of the myriad inputs to an ML application (e.g., data versions, code and tuning parameters), reproducing results, and production deployment. In this paper, we summarize these challenges from our experience with Databricks customers, and describe MLflow, an open source platform we recently launched to streamline the machine learning lifecycle. MLflow covers three key challenges: experimentation, reproducibility, and model deployment, using generic APIs that work with any ML library, algorithm and programming language. The project has a rapidly growing open source community, with over 50 contributors since its launch in June 2018.
@article{zaharia_accelerating_2018,
title = {Accelerating the {Machine} {Learning} {Lifecycle} with {MLflow}},
volume = {41},
abstract = {Machine learning development creates multiple new challenges that are not present in a traditional software development lifecycle. These include keeping track of the myriad inputs to an ML application (e.g., data versions, code and tuning parameters), reproducing results, and production deployment. In this paper, we summarize these challenges from our experience with Databricks customers, and describe MLflow, an open source platform we recently launched to streamline the machine learning lifecycle. MLflow covers three key challenges: experimentation, reproducibility, and model deployment, using generic APIs that work with any ML library, algorithm and programming language. The project has a rapidly growing open source community, with over 50 contributors since its launch in June 2018.},
language = {en},
number = {4},
journal = {IEEE Data Engineering Bulletin},
author = {Zaharia, Matei and Chen, Andrew and Davidson, Aaron and Ghodsi, Ali and Hong, Sue Ann and Konwinski, Andy and Murching, Siddharth and Nykodym, Tomas and Ogilvie, Paul and Parkhe, Mani and Xie, Fen and Zumar, Corey},
month = dec,
year = {2018},
keywords = {⛔ No DOI found},
pages = {7},
}
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