A Data Source Dependency Analysis Framework for Large Scale Data Science
Projects
- URL: http://arxiv.org/abs/2212.07951v1
- Date: Thu, 15 Dec 2022 16:34:39 GMT
- Title: A Data Source Dependency Analysis Framework for Large Scale Data Science
Projects
- Authors: Laurent Bou\'e and Pratap Kunireddy and Pavle Suboti\'c
- Abstract summary: Data source dependency hell refers to the central role played by data and its unique quirks that often lead to unexpected failures of machine learning models.
We present an automated dependency mapping framework that allows MLOps engineers to monitor the whole dependency map of their models in a fast paced engineering environment.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Dependency hell is a well-known pain point in the development of large
software projects and machine learning (ML) code bases are not immune from it.
In fact, ML applications suffer from an additional form, namely, "data source
dependency hell". This term refers to the central role played by data and its
unique quirks that often lead to unexpected failures of ML models which cannot
be explained by code changes. In this paper, we present an automated dependency
mapping framework that allows MLOps engineers to monitor the whole dependency
map of their models in a fast paced engineering environment and thus mitigate
ahead of time the consequences of any data source changes (e.g., re-train
model, ignore data, set default data etc.). Our system is based on a unified
and generic approach, employing techniques from static analysis, from which
data sources can be identified reliably for any type of dependency on a wide
range of source languages and artefacts. The dependency mapping framework is
exposed as a REST web API where the only input is the path to the Git
repository hosting the code base. Currently used by MLOps engineers at
Microsoft, we expect such dependency map APIs to be adopted more widely by
MLOps engineers in the future.
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