AI News, Building and deploying large-scale machine learning applications
- On Monday, June 4, 2018
- By Read More
Building and deploying large-scale machine learning applications
Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.
In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee).
He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world.
In recent years Bickson has turned his focus toward helping companies deploy machine learning systems in production in a wide range of real-world settings.
… Once we started to package algorithms on top of the framework, then we became way more popular because people wanted to use pre-made building blocks.
… When we started to compete using our code, we actually did something that was counter-intuitive: we shared our code during the competition, and then people, if they downloaded it, could improve their own results.
… [where] you also know the day of the week, and the time, and which type of iPhone the user had, and what the user's age and zip code are, what the item color and price is, and so on.
But the good news is that we hired some of the people behind MXNet, which is one of the emerging deep learning platforms, and there you have a lot of other algorithms, including RNN, and you also have features like support of multiple GPUs.
- On Wednesday, January 22, 2020
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