It's never been easier to wrangle and extract value from huge data… but how should a startup think about getting value from data streams that are just getting started, or find insights about small partitions of data for individual customers?
I'll present on how we've tackled these challenges at frame.ai, where we're focused on providing realtime insights about realtime chat streams within one company at a time. I'll talk about setting metrics focused on time-to-value for customers, leveraging model stacks trained on larger data sets, and adopting NLP analysis techniques that behave well where data is sparse.
George Davis has been building technology to extract insights from fancy data streams since the best term for that work was "scientific computing". Having cut his teeth building data systems for bioinformatics, security, and automated trading, he most recently headed the data science organization at Knewton, where his team's work helped guide delivery of educational content to over 10M students. As co-founder of frame.ai, George works with his team to build systems that understand and improve business conversations in team chat.
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