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Machine Learning Tech Debt

They identify several ways in which machine learning can incur significant costs for long term. The paper hidden technical debt in machine learning systems talks about technical debt and other ml specific debts that are hard to detect or hidden.

Technical Debt In Machine Learning Towards Data Science

Organizing Machine Learning Projects Project Management

Big Data Expo Machine Learning In The Elastic Stack

That is machine learning models are machines for creating entanglement and making the isolation.

Machine learning tech debt. Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. In this secti on we look at several issues of this form. Machine learning offers a fantastically powerful toolkit for building useful complexprediction systems quickly. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. You very much get the. Hidden technical debt in machine learning systems d. In a paper google engineers have pointed out the various costs of maintaining a machine learning system. Experienced teams know when to back up seeing a piling debt but technical debt in machine learning piles extremely fast. This paper argues it is dangerous to think ofthese quick wins as coming for free. Using the framework of technical debt we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. Using the software engineering frameworkof technical debt we find it is common to incur massive ongoing maintenancecosts in real world ml systems. 21 entanglement from a high level perspective a machine learning package is a tool for mixing data sources together. This paper argues it is dangerous to think of these quick wins as coming for free. The high interest credit card of technical debt sculley et al. That is because the technical debt has a compound effect.

This article discusses three of the technical debts that you may encounter in your journey to production. 2014 todays paper offers some pragmatic advice for the developers and maintainers of machine learning systems in production. Its easy to rush out version 10 the authors warn us but making subsequent improvements can be unexpectedly difficult. Cause signicant increases in technical debt. The problem with the technical debt though is the same as with the financial debt when the time comes to pay the debt we give back more than we took at the beginning. Specifically it has all the problems of regular code plus ml specific issues at the system level. Using the software engineering framework of technical debt we find it is common to incur massive ongoing maintenance costs in real world ml systems. Artificial intelligence ai and machine learning ml systems have a special ability to increase technical debt. In 34 the authors consider the hidden costs of machine learning with the idea of technical debt. They found that is common to incur massive maintenance costs in real world machine learning systems.