NVIDIA and SAP hosted their first Women In Big Data community event, AI Connect, on October 18, 2017. More than 100 participants met up at the NVIDIA campus in Santa Clara to learn about the latest advancements in deep learning
Talk 1: Deep Learning Use Cases
Renee Yao kicked things off, presenting seven deep learning use cases that highlight how different companies use different deep learning technologies to solve their customers’ problem.
Talk 2: Deep Learning Workflows
Kari Briski, director of Deep Learning Software Products at NVIDIA, talked about deep learning workflows. She went into detail on the NVIDIA SDK, deep learning training stack, and specific GPU benchmark results, and she laid out a step-by-step guide to deep learning workflows.
TensorRT, which maximizes inference throughput for latency critical services, was one of the highlights of her talk. Customers are thrilled about TensorRT performance:
The audience in particular enjoyed Kari’s live demo, where she showed how we used transfer learning to train a model and deploy the edge in under a minute. Kari used DIGITS and Volta Tensor Core accelerated training on DGX Station (an AI personal supercomputer) to re-train GoogleNet (with 7 classes) in 15 epochs in 35 seconds and immediately deployed a new model to the edge into the Jetson TX2 developer kit.
Talk 3: Deep Learning In Enterprise
Wrapping up the evening, SAP Data Scientist Nazanin Zaker spoke about Deep Learning in the Enterprise. She took us through how deep learning works by breaking down what features, classifiers and CNNs are. She also introduced SAP Leonardo Digital innovation System for machine learning applications.
One of the demos she showed was FaceAPI (a rest API available in the SAP Leonardo Machine Learning foundation), which uses CNNs to detect the position of faces in input images. This API is already available for developers to add to their code and can be called using a post request. An example research paper related to this topic is provided in the slides for more information (see below).
Following that demo, she talked about Text API, which is another functional service available in SAP Leonardo Machine Learning and accessible by a post request. This API can do sentiment analysis on an input text and return the result as positive, negative or neutral. CNN models were applied in this API as well. An example of how CNNs can be used for text processing can be found in the slides.
Nazanin concluded with a few examples, one of which illustrated how Swarovski leverages machine learning and computer vision to repair broken products. Swarovski handles more than 100 service tickets and images a day, and previously spent manual effort going through a catalog with more than 40,000 products. Now, with SAP’s solution, they can compare the broken image with images in the product catalog in seconds.
In another example, SAP Brand Impact uses deep learning to detect logos in an input video, as well as to compare detected logos based on the their frequency and latency in an input video. This application can establish brand awareness within seconds instead of days or weeks.
Overall, feedback on the event was extremely positive. If you’re eager to get started, we shared some resources at the end of each talk. Here are a few:
Thanks to all who made the event possible, and we look forward to seeing everyone at out next Meetup.