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Machine Learning Approaches for Startups That Lack Data

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Startups interested in creating data products through machine learning face a daunting task, lack of data. Many get caught up in a vicious cycle - no data leads to few users and not having users means not being able to collect interesting data. One attempt to overcome this cold start problem is through transfer learning. Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned. We will explore ways to leverage available datasets to bootstrap machine learning models in startups that have not yet gathered their own datasets. We will cover use cases from startups in enterprise collaboration and fintech. The methods discussed will include feature learning, learning to rank, hierarchical transfer, etc.

by Arshak Navruzyan, Startup.ML
Arshak is the founder of the machine learning incubator Startup.ML. Startup.ML brings machine learning to startups while training the next generation of data scientists. Prior to Startup.ML, Arshak held senior product management and technology leadership roles at Argyle Data, Alpine Data Labs, Endeca/Oracle.