Data Council Blog

Data Council Blog

Wootric - Featured Startup SF '18

In this blog series leading up to our SF18 conference, we invite our featured startups to tell us more about their data engineering challenges. Today, we speak with Wootric, an early-stage company building customer experience tools powered by machine learning.

Q:  What surprised you most as an engineer about the work you did that you'll be telling us about in your talk? 
Prabhat JhaBuilding text classification algorithms in a generic way is really hard. We had to narrow down our problem to a smaller subset to get a meaningful result with a reasonable amount of effort.

Q: What do you think a listener will get out of this this talk vs. other talks on distributed data processing and data versioning that they've previously heard?

Prabhat Jha: Versioning best practices from traditional software development do not apply to machine learning because models are part of code. As you create ML models based on training data, and as you change the algorithms and code behind the models, you will have different versions of models that you have to be aware of when thinking about versioning.
Q: Is there any additional back-story about you and your company that you want our audience to know?
Prabhat Jha: Wootric has collected millions of customer feedback data points in last 3 years from most of the verticals and industries you can think of. In particular, SaaS and DevOps are our sweet spot.

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About the Startups Track

The data-oriented Startups Track at DataEngConf features dozen of startups forging ahead with innovative approaches to data and new data technologies. We find the most interesting startups at the intersection of ML, AI, data infrastructure and new applications of data science and highlight them in technical talks by their CTOs and lead engineers who are building these platforms. 

Data Engineering, Data Warehouse, Data Strategy

Robert Winslow

Written by Robert Winslow

Robert is a seasoned software consultant with a decade of experience shipping great products. He thrives in early-stage startup environments, and works primarily in Go, Python, and Rust. He has led backend development at companies like RankScience and; created a rigorous, open-source time-series benchmarking suite for InfluxData; and rapidly prototyped software in a skunkworks-type product lab. He’s taught graduate statistics at GalvanizeU and mentored at the Stanford He helps maintain Google’s FlatBuffers project, one of the world’s fastest serialization libraries. A colleague once described him as “the developer equivalent of ‘The Wolf’ from Pulp Fiction."