Yelp runs hundreds of experiments to ensure new features within its apps and website remain aligned with key business metrics. To launch, manage, and analyze the results of these experiments, the company's employees use Bunsen, a proprietary platform developed just under two years ago. During an interview on Wednesday at VentureBeat's Transform summit, Justin Norman, head of data science at Yelp, explained that Bunsen was born out of necessity. Historically, Yelp engineers themselves were responsible for experimentation, which meant they had to write custom code to compare the performance of different versions of products. As the company's portfolio grew over the years, this piecemeal approach became inefficient and expensive.

Yelp is home to nearly 200 million user-submitted reviews and even more photos. This data is rich with information about businesses and user opinions. Through the application of cutting-edge machine learning techniques, we're able to extract and share insights from this data. In particular, the Popular Dishes feature leverages Yelp's deep data to take the guesswork out of what to order. The Popular Dishes feature highlights the most talked about and photographed dishes at a restaurant, gathering user opinions and images in one convenient place.

Thanks to the supercomputer, SMU's food AI project has successfully recognised the 100 most popular dishes in Singapore. The Singapore Management University (SMU) will be using the NVIDIA DGX1 deep learning supercomputer to conduct AI research for Singapore's Smart Nation project. The research will take place at the SMU Living Analytics Research Centre (LARC), which is supported and funded by Singapore's National Research Foundation (NRF). Providing performance that is equivalent to 250 conventional servers, the supercomputer will reduce the time it takes for researchers to train sophisticated deep neural networks. The solution is also built on NVIDIA Tesla P100 GPUs, which utilise the latest Pascal GPU architecture.