Filtering by: Day2 Afternoon1 ScalingML
View Event →
Machine learning as a service (MLaS) is imperative to the success of many companies as many internal teams and organizations need to gain business intelligence from big data. Building a scalable MLaS in a very challenging problem. In this paper, we present the scalable MLaS we built for a company that operates globally. We focus on several scalability challenges and our technical solutions.
LI Erran Li received his Ph.D. in Computer Science from Cornell University in 2001. From 2001 to 2015, he worked as a researcher in Bell Labs, Alcatel-Lucent (acquired by Nokia). Since 2015, he started working as a senior software engineer at Uber Technologies. He is also an adjunct professor in the Computer Science Department of Columbia University. He is an IEEE Fellow and ACM Distinguished Scientist. His research interests are machine learning algorithms, systems, deep learning and AI.
Linkedin - Website
View Event →
The development of predictive models is a time and computationally intensive process that is highly iterative in nature. By carefully optimizing the right parts of the workflow, order of magnitude type speed-ups can be achieved, leading to more accurate models in shorter periods of time. In this talk we'll touch on several different ways in which we've been able to drastically reduce the time to train deep learning models, from high level library choices all the way down to leveraging custom silicon.
Scott has over nine years experience creating machine learning based solutions to solve large-scale, real-world problems. Scott's currently the cloud team lead at Nervana Systems, focused on providing a highly optimized deep learning platform for customers across a variety of domains. Inside of work he can often be found pushing and reviewing code. Outside of work he can often be found running long distances and quaffing local craft beer, occasionally simultaneously.
View Event →
In this talk, we propose prediction techniques using deep learning on fashion images. We show how to build a generic deep learning model, which could be used with a fashion image to predict the clothing type in that image and generate fashion image description/captions. We propose a method to apply a pre-trained deep convolution neural network (DCNN) on images to improve prediction accuracy. We use an ImageNet pre-trained DCNN and apply fine-tuning to transfer the learned features to the prediction.
Anusua Trivedi is a Data Scientist at Microsoft’s Advanced Data Science & Strategic Initiatives team. She works on developing advanced Predictive Analytics & Deep Learning models. Prior to joining Microsoft, Anusua was a data scientist at a Supercomputer Center - Texas Advanced Computing Center (TACC). Anusua is a frequent speaker at machine learning and big data conferences.
Twitter: @anurive - Linkedin - Website