Hidden Technical Debt in Machine Learning Systems
Authors: D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
NeurIPS 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper does not offer novel ML algorithms, but instead seeks to increase the community s awareness of the difficult tradeoffs that must be considered in practice over the long term. We focus on system-level interactions and interfaces as an area where ML technical debt may rapidly accumulate. |
| Researcher Affiliation | Industry | D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips EMAIL Google, Inc. Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Franc ois Crespo, Dan Dennison EMAIL Google, Inc. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper is a conceptual discussion of technical debt in ML systems and does not present a specific methodology or tool for which source code would be released. It does not mention any open-source code for its own described work. |
| Open Datasets | No | The paper is a conceptual discussion of technical debt in ML systems and does not describe experiments that use a specific dataset for training or evaluation. It references general practices and systems but not specific public datasets used in its own research. |
| Dataset Splits | No | The paper is a conceptual work and does not conduct experiments, therefore, it does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper is a conceptual discussion of technical debt in ML systems and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is a conceptual discussion and does not describe any specific software implementation details or experiments that would require listing software dependencies with version numbers. |
| Experiment Setup | No | The paper is a conceptual discussion and does not describe any experiments or specific model training, thus it does not provide details about experimental setup or hyperparameters. |