Bing revealed today that it has been using BERT in search results before Google, and it’s also being used at a larger scale. The Computer City
Google’s use of BERT in search results is currently affecting 10% of search results in the US, as well as featured snippets in two dozen countries. Bing, on the other hand, is now utilizing BERT worldwide.
Bing has been using BERT since April, which was about half a year before Google. In a blog post, Bing details the challenges he faced when implementing BERT in the global search results.
The application of a deep learning model such as BERT to web search worldwide can be prohibitively expensive, Bing admits. It was eventually possible with Microsoft’s “Azure” cloud computing service.
What was initially estimated to require tens of thousands of servers to achieve this was achieved with more than 2000 Azure GPU virtual machines. Bing went from serving a three-layer BERT model in 20 CPU cores at 77 milliseconds per inference, to serving 64 inferences in 6 milliseconds using a GPU model in an Azure virtual machine. Bing is Now Utilizing BERT at a Larger Scale
The switch from running BERT on a CPU model to running BERT on a GPU model lead to a 800x increase in throughput improvement:
“With these GPU optimizations, we were able to use 2000+ Azure GPU Virtual Machines across four regions to serve over 1 million BERT inferences per second worldwide. Azure N-series GPU VMs are critical in enabling transformative AI workloads and product quality improvements for Bing with high availability, agility, and significant cost savings, especially as deep learning models continue to grow in complexity.”
These improvements to Bing search are available globally as of today. For more information on BERT,
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