WebUsing the software engineering frameworkof technical debt, we find it is common to incur massive ongoing maintenancecosts in real-world ML systems. We explore several ML … Web15 de mar. de 2024 · Much of the discussions in the AI/ML space revolve around model development. As shown in this diagram from the canonical Google paper “ Hidden Technical Debt in Machine Learning Systems ”, the bulk of activities, time and expense in building and managing ML systems is not in Model training, but in the myriad ancillary …
Hidden Technical Debt in Machine Learning Systems - Random …
Web7 de mai. de 2024 · Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today's data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical … WebCutting Debts. The above-mentioned scenarios are one of the many technical debts that might get induced into an ML system. Configuration debt, data dependency debt, monitoring, management debt and many more. The collection of these debts become more sophisticated as ecosystems support multiple models together. So, it is advisable to be … sharper service appliance repair
An Empirical Study of Refactorings and Technical Debt in Machine ...
Web7 de jul. de 2024 · As rosy as it may seem at first, it is accumulating hidden technical debt in terms of maintaining such machine learning systems. But let's first understand what a technical debt is: “In software development, technical debt (also known as design debt or code debt) is the implied cost of additional rework caused by choosing an easy (limited ... Web11 de jul. de 2024 · “Hidden Technical Debt in Machine Learning Systems,” a peer-reviewed article published in 2015 and based on insights from dozens of machine learning practitioners at Google, advises that ... Webhidden debt. Thus, refactoring these libraries, adding better unit tests, and associated activity is time well spent but does not necessarily address debt at a systems level. In this paper, we focus on the system-level interaction between machine learning code and larger sys-tems as an area where hidden technical debt may rapidly accumulate. sharper service solutions