Filtering by: Scaling ML

Scaling Machine Learning as a Service — LI Erran Li (Uber)
Oct
11
2:30 PM14:30

Scaling Machine Learning as a Service — LI Erran Li (Uber)

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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A multi-pronged approach to speeding up predictive applications — Scott Leishman (Intel / Nervana Systems)
Oct
11
2:00 PM14:00

A multi-pronged approach to speeding up predictive applications — Scott Leishman (Intel / Nervana Systems)

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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Transfer Learning and Fine-tuning Deep Convolution Neural Network model for Fashion images — Anusua Trivedi (Microsoft)
Oct
11
1:30 PM13:30

Transfer Learning and Fine-tuning Deep Convolution Neural Network model for Fashion images — Anusua Trivedi (Microsoft)

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

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Meta Data Science: When all the world's data scientists are just not enough — Chalenge Masekera (Salesforce)
Oct
10
2:30 PM14:30

Meta Data Science: When all the world's data scientists are just not enough — Chalenge Masekera (Salesforce)

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.

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Bringing the power of Spark to all data analysts — Clément Stenac (Dataiku)
Oct
10
2:00 PM14:00

Bringing the power of Spark to all data analysts — Clément Stenac (Dataiku)

A few years ago, Hive brought SQL to Hadoop and enabled its widespread adoption by data analysts. Today, Spark has become the tool of choice for data engineers, who can build powerful data pipelines. However, Spark is fairly complex. Using it efficiently requires some understanding of the inner workings (shuffler, caching, memory, …). We will cover the challenges we faced in bringing Spark to an audience of less technical users, some of the solutions (like auto-tuning), and how improvements to Spark (memory management, statistics, new APIs, …) help bring its power to every data citizen.

Clément Stenac is a passionate software engineer, CTO of Dataiku. We are the makers of DSS, an integrated development environment that helps data analysts, scientists and engineers collaborate to build and run data applications. Clément was previously head of development at Exalead, leading the design and implementation of large-scale search engine software. He also has extended experience with open source software, as a former developer of the VideoLAN (VLC) and Debian projects.

Twitter: @ClementStenac - Linkedin

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Automating Machine Learning — Poul Petersen (BigML)
Oct
10
1:30 PM13:30

Automating Machine Learning — Poul Petersen (BigML)

In just the last few years, Machine Learning has gone from something barely known outside of academic circles, to becoming now a critically important tool for optimizing business operations. Assuming an organization even has a small team of ML experts, as the number of ML applications explodes, the pressure on these teams and their hand tailored solutions brings innovation to a halt.

As a result, many organizations are beginning to realize that the solution is to bring ML to everyone as a standardized platform. So, what should you be looking for in a platform? As easy as it it to make a wishlist of features, it's equally easy to overlook the importance of automation. ML tasks are iterative by nature and automation of the tasks and workflows is essential. In this talk you will see how WhizzML is making automation easy, reducing the need for experts, and putting the Machines back into Machine Learning.

Poul is Chief Infrastructure Officer at BigML. He has an MS degree in Mathematics as well as BS degrees in Mathematics, Physics and Engineering Physics. With 20 plus years of experience building scalable and fault tolerant systems in data centers, Poul currently enjoys the benefits of programmatic infrastructure, hacking in python to run BigML with only a laptop and a cloud.

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