Presentation: Counting is Hard: Probabilistic Algorithms for View Counting at Reddit

Track: Handling Sequential Data Like an Expert / ML Applied to Operations

Location: Cyril Magnin II

Duration: 10:55am - 11:45am

Day of week: Wednesday

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Abstract

While counting votes has always been a core feature of Reddit's platform, only recently did we begin counting and displaying view numbers. In this talk, we explain the challenges of building a view counting system at scale, and how we used probabilistic counting algorithms to make scaling easier.

Speaker: Krishnan Chandra

Data Engineer @Reddit

Krishnan is a data engineer at Reddit, and has been working in data engineering for 4 years. Before joining Reddit, Krishnan worked on backend engineering at Optimizely and LinkedIn. He holds bachelor's degrees in computer science and math from the University of Illinois at Urbana-Champaign.

Find Krishnan Chandra at

Tracks

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    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.