Track:

ML in Action

Day of week: Tuesday

Applied Machine Learning track demonstrating how to train, score, and handle security and fraud use cases.

Track 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 RiskSalon.org, a round-table forum for risk professionals in San Francisco to share ideas on stopping bad actors.


by Holden Karau
Spark Committer

Machine Learning is super cool, but what about those of us who maybe got a D in statistics (or maybe didn't bother taking the class). With modern systems, it's relatively simple to train a model regardless of your background, but how do you know if the model you've trained does the "right" thing and how do you actually use your model?
This talk will explore how to train models (using big data because that's what the presenter works with, but it will work just fine on small data as well...


by Michael Manapat
Head of Conversion Products @Stripe

Stripe processes billions of dollars in payments a year and uses machine learning to detect and stop fraudulent transactions. Like models used for ad and search ranking, Stripe's models don't just score—they dictate actions that directly change outcomes. High-scoring transactions are blocked before they can ever get refunded or disputed by the card holder. Deploying an initial model that successfully blocks a substantial amount of fraud is a great first step, but since your model is altering...


by Anish Das Sarma
Engineer Manager @Airbnb

Presentation details will be available soon.


by Jevin Bhorania
Cash Data Science Lead @Square

Presentation details will be available soon.


More details to come.


More details to come.


More details to come.


More details to come.

Tracks

  • 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.