Co-authors: Keqiu Hu, Jonathan Hung, Haibo Chen, and Sriram Rao At LinkedIn, we use Hadoop as our backbone for big data analytics and machine learning. With an exponentially growing data volume, and the company heavily investing in machine learning and data science, we have been doubling our cluster size year over year to match the compute workload growth. Our...
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- Topics:
- Hadoop,
- Data,
- Open Source
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Co-authors: James Verbus and Beibei Wang The Anti-Abuse AI Team at LinkedIn creates, deploys, and maintains models that detect and prevent many types of abuse, including the creation of fake accounts, member profile scraping, automated spam, and account takeovers. As we prevent abuse using machine learning, there are several challenges we can face: Maximizing...
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Co-authors: Ze Mao, Matt Wise, Casey Getz, Justin Lin, Ashish Singhai, and Rob Block Introduction Ambry is LinkedIn's scalable geo-distributed object store. Developed in-house and open sourced in 2016, Ambry stores tens of petabytes of data. At LinkedIn, Ambry is used to store objects like photos, videos, and resume uploads, as well as internal binary data....
- Topics:
- infrastructure,
- Data,
- Storage,
- Open Source
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Co-authors: Kirill Talanine, Jeffrey D. Gee, Rohan Ramanath, Konstantin Salomatin, Gungor Polatkan, Onkar Dalal, and Deepak Kumar...
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Co-authors: Crystal Hsieh, Kyle Huynh, Sameera Padhye, Kalyan Punukollu, Federico Hlawaczek, Pavan Mellamputi, and Christian Byza As...
- Topics:
- Video,
- Product Design
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Co-authors: Kinjal Basu, Yao Pan, Rohan Ramanath, Konstantin Salomatin, Amol Ghoting, and S. Sathiya Keerthi Building thriving...
- Topics:
- artificial intelligence,
- scale,
- machine learning,
- Open Source