[A version of this post appears on the O’Reilly Radar.]
The O’Reilly Data Show Podcast: Jason Dai on the first year of BigDL and AI in China.
In this episode of the Data Show, I spoke with Jason Dai, CTO of Big Data Technologies at Intel, and one of my co-chairs for the AI Conference in Beijing. I wanted to check in on the status of BigDL, specifically how companies have been using this deep learning library on top of Apache Spark, and discuss some newly added features. It turns out there are quite a number of companies already using BigDL in production, and we talked about some of the popular uses cases he’s encountered. We recorded this podcast while we were at the AI Conference in Beijing, so I wanted to get Dai’s thoughts on the adoption of AI technologies among Chinese companies and local/state government agencies.
Here are some highlights from our conversation:
BigDL: One year later
Big DL was actually first open-sourced on December 30, 2016—so it has been about 1 year and 4 months. We have gotten a lot of positive feedback from the open source community. We also added a lot of new optimizations and functionalities to Big DL. I think it roughly can be categorized into four classes. We did large optimizations, especially for the big data environment, which is essentially very large-scale Intel server clusters. We use a lot of hardware accelerations and Math Kernel librariesto improve BigDL’s performance on a single-node. At the same time, we leverage the Spark architecture so that we can efficiently scale out and perform very large-scale distributed training or inference.
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