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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.
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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.
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What if you had to build more models than there are data scientists in the world? Well, enterprise companies serving hundreds of thousands of businesses often have to do precisely this. In this talk, I'll describe our general purpose machine learning platform that automatically builds per-company optimized models for any given predictive problem at scale, beating out most hand tuned models.
Chalenge Masekera is a data scientist at Salesforce, where he builds machine learning models and analytics tools that enable real time monitoring of system infrastructure, machine learning models and executive dashboards ensuring scalable machine learning pipelines. Previous experience also includes business intelligence consultancy. He has a Masters in Information Management and Systems from the University of California, Berkeley.