2015 ScalingMachineLearningandStatis

• (Agarwal, 2015) ⇒ Deepak Agarwal. (2015). “Scaling Machine Learning and Statistics for Web Applications.” Invited Talk at the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2790452

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Scaling+Machine+Learning+and+Statistics+for+Web+Applications
• http://dl.acm.org/citation.cfm?id=2783258.2790452&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; machine learning; statistical modeling; web applications

Abstract

Scaling web applications like recommendation systems, search and computational advertising is challenging. Such systems have to make astronomical number of decisions every day on what to serve users when they are visiting the website and/or using the mobile app. Machine learning and statistical modeling approaches that can obtain insights by continuously processing large amounts of data emitted at very high frequency by these applications have emerged as the method of choice. However, there are three challenges to scale such methods: a) scientific b) infrastructure and c) organizational. I will provide an overview of these challenges and the strategies we have adopted at LinkedIn to address those. Throughout, I will illustrate with examples from real-world applications at LinkedIn.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 ScalingMachineLearningandStatis	Deepak Agarwal			Scaling Machine Learning and Statistics for Web Applications				10.1145/2783258.2790452		2015