A pervasive but often overlooked problem in predictive modeling on real-life data is the problem of data or label leakage. At Enterprise companies such as Salesforce that provide ML-as-a-service to other businesses, 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. In this talk, we will describe how we tackled this problem at Salesforce scale, where we need to churn out thousands of personalized customer-specific machine learning models for any given use case. The automated approaches we describe 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".
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. Before Salesforce, he obtained a PhD in Cognitive Science at the University of California, Merced, where he studied collaboration patterns of academics using NLP techniques.
Leah McGuire is a Principal Member of Technical Staff at Salesforce Einstein, building platforms to enable the integration of machine learning into Salesforce products. Before joining Salesforce, Leah was a Senior Data Scientist on the data products team at LinkedIn working on personalization, entity resolution, and relevance for a variety of LinkedIn data products. She completed a PhD and a Postdoctoral Fellowship in Computational Neuroscience at the University of California, San Francisco, and at University of California, Berkeley, where she studied the neural encoding and integration of sensory signals.
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