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Presentation: When Do You Use ML vs. a Rules Based System?

Track: ML in Action

Location: Cyril Magnin III

Duration: 10:40am - 10:50am

Day of week: Tuesday

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When you have a hammer, everything looks like a nail. In this talk, I will provide examples of applications where machine learning makes sense and when it doesn't. I will motivate the discussion by providing examples from real-world applications in the risk domain (anti-fraud, cyber security, account takeover detection).

Host: Soups Ranjan

Director of Data Science @Coinbase

Soups Ranjan is the Director of Data Science at Coinbase, one the largest bitcoin exchanges in the world. He manages the Risk & Data Science team that is chartered with preventing avoidable losses to the company due to payment fraud or account takeovers. Soups has a PhD in ECE on network security from Rice University. He has previously led the development of Machine Learning pipelines to improve performance advertising at Yelp and Flurry. He is the founder of, a round-table forum for risk professionals in San Francisco to share ideas on stopping bad actors.

Find Soups Ranjan at

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.