Embracing a Taxonomy of Types to Simplify Machine Learning

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Salesforce has created a machine learning framework on top of Spark ML that builds personalized models for businesses across a range of applications. Hear how expanding type information about features has allowed them to deal with custom datasets with good results.

By building a platform that automatically does feature engineering on rich types (e.g. Currency and Percentages rather than Doubles; Phone Numbers and Email Addresses rather than Strings), they have automated much of the work that consumes most data scientists’ time. Learn how you can do the same by building a single model outline based on the application, and then having the framework customize it for each customer.

Session hashtag: #SFds2

Leah McGuire, Technical Staff at

About Leah

Leah McGuire is a Lead Member of Technical Staff at Salesforce, 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.