Gary R. Holt
Authored Publications
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Machine Learning: The High Interest Credit Card of Technical Debt
D. Sculley
Eugene Davydov
Todd Phillips
Dietmar Ebner
Vinay Chaudhary
Michael Young
SE4ML: Software Engineering for Machine Learning (NIPS 2014 Workshop)
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Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these 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. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored
where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.
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Ad Click Prediction: a View from the Trenches
D. Sculley
Michael Young
Dietmar Ebner
Julian Grady
Lan Nie
Todd Phillips
Eugene Davydov
Sharat Chikkerur
Dan Liu
Arnar Mar Hrafnkelsson
Tom Boulos
Jeremy Kubica
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2013)
Preview abstract
Predicting ad click--through rates (CTR) is a massive-scale learning
problem that is central to the multi-billion dollar online
advertising industry. We present a selection of case studies and
topics drawn from recent experiments in the setting of a deployed
CTR prediction system. These include improvements in the context of
traditional supervised learning based on an FTRL-Proximal online
learning algorithm (which has excellent sparsity and convergence
properties) and the use of per-coordinate learning rates.
We also explore some of the challenges that arise in a real-world
system that may appear at first to be outside the domain of
traditional machine learning research. These include useful tricks
for memory savings, methods for assessing and visualizing
performance, practical methods for providing confidence estimates
for predicted probabilities, calibration methods, and methods for
automated management of features. Finally, we also detail several
directions that did not turn out to be beneficial for us, despite
promising results elsewhere in the literature. The goal of this
paper is to highlight the close relationship between theoretical
advances and practical engineering in this industrial setting, and
to show the depth of challenges that appear when applying
traditional machine learning methods in a complex dynamic system.
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