Operationalizing Machine Learning: An Interview Study

• URL: http://arxiv.org/abs/2209.09125v1
• Date: Fri, 16 Sep 2022 16:59:36 GMT
• Title: Operationalizing Machine Learning: An Interview Study
• Authors: Shreya Shankar, Rolando Garcia, Joseph M. Hellerstein, Aditya G. Parameswaran
• Abstract summary: We conduct semi-structured interviews with 18 machine learning engineers (MLEs) working across many applications. Our interviews expose three variables that govern success for a production ML deployment: Velocity, Validation, and Versioning. We summarize common practices for successful ML experimentation, deployment, and sustaining production performance.
• Score: 13.300075655862573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps, consists of a continual loop of (i) data collection and labeling, (ii) experimentation to improve ML performance, (iii) evaluation throughout a multi-staged deployment process, and (iv) monitoring of performance drops in production. When considered together, these responsibilities seem staggering -- how does anyone do MLOps, what are the unaddressed challenges, and what are the implications for tool builders? We conducted semi-structured ethnographic interviews with 18 MLEs working across many applications, including chatbots, autonomous vehicles, and finance. Our interviews expose three variables that govern success for a production ML deployment: Velocity, Validation, and Versioning. We summarize common practices for successful ML experimentation, deployment, and sustaining production performance. Finally, we discuss interviewees' pain points and anti-patterns, with implications for tool design.

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