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Past Presentations
Engineering Systems for Real-Time Predictions @DoorDash
Today, applying machine learning to drive business value in a company requires a lot more than figuring out the right algorithm to use; it requires tools and systems to manage the entire machine learning product lifecycle. For instance, we need systems to manage data pipelines, to monitor model...
Closer to the Wire: Real-time News Alerting @Bloomberg
What is it worth to be the first person to read a news story? For a trader, a second is enough to make or break a portfolio. Bloomberg clients leverage our highly customizable real-time news alerts to make informed trading decisions. But how do you build such a flexible alerting system that...
Interviews
Engineering Systems for Real-Time Predictions @DoorDash
QCon: Can you describe the machine learning platform you have leverage at DoorDash?
Raghav: We built our system around common machine learning open source libraries in Python like SciKit-Learn, LightGBM, and Keras. We have a microservices architecture also built in Python which includes a prediction service that handles all the predictions and a features service. All the services are hosted on AWS.
Read Full InterviewCloser to the Wire: Real-time News Alerting @Bloomberg
What is the focus of the work you do today?
I'm working in the News Search Infrastructure at Bloomberg. In a nutshell, when a news story hits the News Search Infrastructure, it gets enriched by a set of metadata, e.g. classification, people tagging, sentiment analysis, etc, then it is indexed to be available for searching. What is interesting is that the incoming news stories...
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