Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
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How to train your model (and catch label leakage)

Till Bergmann (Salesforce)
2:40pm3:20pm Thursday, March 28, 2019
Average rating: ***..
(3.67, 6 ratings)

Who is this presentation for?

  • Data scientists and product managers for ML/DS products



Prerequisite knowledge

  • A basic understanding of machine learning models and messy data

What you'll learn

  • Understand why label leakage is an enormous problem in enterprise machine learning
  • Learn how Salesforce solves this problem using open source libraries


A pervasive but often overlooked problem in predictive modeling on real-life data is the problem of data or label leakage. At enterprise companies that provide ML as a service to other businesses, such as Salesforce, this problem takes on monstrous proportions as the data is populated by diverse and often unknown business processes, making it very hard for data scientists to distinguish cause from effect.

Till Bergmann explains how Salesforce—which needs to churn out thousands of customer-specific models for any given use case—tackled this problem. The automated approaches are a part of our recently open-sourced Spark-based library TransmogrifAI and extend the boundaries of what typically falls in the domain of automated machine learning.

Photo of Till Bergmann

Till Bergmann


Till Bergmann is a senior data scientist at Salesforce Einstein, building platforms to make it easier to integrate machine learning into Salesforce products, with a focus on automating many of the laborious steps in the machine learning pipeline. He holds a PhD in cognitive science from the University of California, Merced, where he studied the collaboration patterns of academics using NLP techniques.