Stratum: A Serverless Framework for the Lifecycle Management of Machine Learning-based Data Analytics Tasks


Anirban Bhattacharjee, Yogesh Barve, Shweta Khare, Shunxing Bao, and Aniruddha Gokhale, Vanderbilt University; Thomas Damiano, Lockheed Martin Advanced Technology Labs


With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task. Furthermore, with a plethora of cloud-based ML model development platforms, heterogeneity in hardware, increased focus on exploiting edge computing resources for low-latency prediction serving and often a lack of a complete understanding of resources required to execute ML workflows efficiently, ML model deployment demands expertise for managing the lifecycle of ML workflows efficiently and with minimal cost. To address these challenges, we propose an end-to-end data analytics, a serverless platform called Stratum. Stratum can deploy, schedule and dynamically manage data ingestion tools, live streaming apps, batch analytics tools, ML-as-a-service (for inference jobs), and visualization tools across the cloud-fog-edge spectrum. This paper describes the Stratum architecture highlighting the problems it resolves.

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@inproceedings {232983,
author = {Anirban Bhattacharjee and Yogesh Barve and Shweta Khare and Shunxing Bao and Aniruddha Gokhale and Thomas Damiano},
title = {Stratum: A Serverless Framework for the Lifecycle Management of Machine Learning-based Data Analytics Tasks},
booktitle = {2019 {USENIX} Conference on Operational Machine Learning (OpML 19)},
year = {2019},
isbn = {978-1-939133-00-7},
address = {Santa Clara, CA},
pages = {59--61},
url = {},
publisher = {{USENIX} Association},
month = may,