Open AccessProceedings Article
Hidden technical debt in Machine learning systems
D. Sculley,Gary Holt,Daniel Golovin,Eugene Davydov,Todd Phillips,Dietmar Ebner,Vinay Chaudhary,Michael Young,Jean-Francois Crespo,Dan Dennison +9 more
- Vol. 28, pp 2503-2511
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TLDR
It is found it is common to incur massive ongoing maintenance costs in real-world ML systems, and several ML-specific risk factors to account for in system design are explored.Citations
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
TL;DR: In this article, the authors propose LIME, a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.
Posted Content
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez,Been Kim +1 more
TL;DR: This position paper defines interpretability and describes when interpretability is needed (and when it is not), and suggests a taxonomy for rigorous evaluation and exposes open questions towards a more rigorous science of interpretable machine learning.
Proceedings Article
Anchors: High-Precision Model-Agnostic Explanations
TL;DR: This work introduces a novel model-agnostic system that explains the behavior of complex models with high-precision rules called anchors, representing local, “sufficient” conditions for predictions, and proposes an algorithm to efficiently compute these explanations for any black-box model with high probability guarantees.
Proceedings ArticleDOI
Software engineering for machine learning: a case study
Saleema Amershi,Andrew Begel,Christian Bird,Robert DeLine,Harald C. Gall,Ece Kamar,Nachiappan Nagappan,Besmira Nushi,Thomas Zimmermann +8 more
TL;DR: A study conducted on observing software teams at Microsoft as they develop AI-based applications finds that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace.
Journal ArticleDOI
Massive MIMO is a reality—What is next?: Five promising research directions for antenna arrays
TL;DR: In this paper, the authors explain how the first chapter of the massive MIMO research saga has come to an end, while the story has just begun, and outline five new massive antenna array related research directions.
References
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Book
Refactoring: Improving the Design of Existing Code
TL;DR: Almost every expert in Object-Oriented Development stresses the importance of iterative development, but how do you add function to the existing code base while still preserving its design integrity?
Proceedings ArticleDOI
Refactoring improving the design of existing code
TL;DR: The present document details the how, why and when to apply refactoring in computer systems that have been poorly designed, this in order to a better performance and maintenance of the constituent components.
Proceedings ArticleDOI
Scaling Distributed Machine Learning with the Parameter Server
TL;DR: View on new challenges identified are shared, and some of the application scenarios such as micro-blog data analysis and data processing in building next generation search engines are covered.
Proceedings ArticleDOI
Scaling distributed machine learning with the parameter server
Mu Li,David G. Andersen,Jun Woo Park,Alexander J. Smola,Amr Ahmed,Vanja Josifovski,James Long,Eugene J. Shekita,Bor-Yiing Su +8 more
TL;DR: In this paper, the authors propose a parameter server framework for distributed machine learning problems, where both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices.
Book
AntiPatterns: Refactoring Software, Architectures, and Projects in Crisis
TL;DR: An entertaining and often enlightening text that defines what seasoned developers have long suspected: despite advances in software engineering, most software projects still fail to meet expectations--and about a third are cancelled altogether.