Presentation: Measuring Business Impact of Machine Learning System

Track: ML in Action

Location: Cyril Magnin III

Duration: 1:10pm - 2:00pm

Day of week: Tuesday

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Abstract

I will provide an overview of how to do metric driven ML system development, primarily to answer following questions:

  • How do you bootstrap machine learning system for new objectives?
  • How do you tie up the ML system performances with business goals?
  • Do precision & recall always work as primary metrics?
  • How do you measure effectiveness of entire ML system in the context of product success?

These questions will be answered in the context of fraud detection case study.

Speaker: Jevin Bhorania

Cash Data Science Lead @Square

Find Jevin Bhorania at

Tracks

  • Deep Learning Applications & Practices

    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.