Technical Debt and Its Hidden Costs in Machine Learning Development
Episode 20, Sep 26, 06:30 AM
In this episode of the AI Paper Club Podcast, hosts Rafael Herrera and Sonia Marques discuss the essential 2015 paper "Hidden Technical Debt in Machine Learning Systems" with Bernardo Ramos. They explore the accumulation of technical debt in machine learning, data dependencies, and strategies to mitigate these challenges.
In this episode of the AI Paper Club Podcast, hosts Rafael Herrera and Sonia Marques sit down with senior machine learning engineer Bernardo Ramos from Deeper Insights. Together, they explore the classic 2015 paper "Hidden Technical Debt in Machine Learning Systems". The paper highlights the often-overlooked issue of technical debt in machine learning projects and how it silently accumulates over time, much like financial debt.
The discussion delves into the nuances of technical debt, particularly how data dependencies differ from code dependencies and why they are harder to detect. The podcast also covers unstable data signals, feedback loops, and the unique challenges faced by large language models (LLMs) in today's data-driven world. Bernardo shares potential mitigation strategies to help manage these technical debts effectively.
A special thank you to the authors D. Sculley, G. Holt, D. Golovin, and their team for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://dl.acm.org/doi/10.5555/2969442.2969519.
For more information on artificial intelligence, machine learning, and engineering solutions for your business, please visit www.deeperinsights.com or contact us at thepaperclub@deeperinsights.com.
The discussion delves into the nuances of technical debt, particularly how data dependencies differ from code dependencies and why they are harder to detect. The podcast also covers unstable data signals, feedback loops, and the unique challenges faced by large language models (LLMs) in today's data-driven world. Bernardo shares potential mitigation strategies to help manage these technical debts effectively.
A special thank you to the authors D. Sculley, G. Holt, D. Golovin, and their team for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://dl.acm.org/doi/10.5555/2969442.2969519.
For more information on artificial intelligence, machine learning, and engineering solutions for your business, please visit www.deeperinsights.com or contact us at thepaperclub@deeperinsights.com.