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Friday, December 8 • 2:45pm - 3:20pm
Democratizing Machine Learning on Kubernetes [I] - Joy Qiao & Lachlan Evenson, Microsoft

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One of the largest challenges facing the machine learning community today is understanding how to build a platform to run common open-source machine learning libraries such as Tensorflow. Both Joy and Lachie are both passionate about making machine learning accessible to the masses using Kubernetes. In this session they'll share how to deploy a distributed Tensorflow training cluster complete with GPU scheduling on Kubernetes. We’ll also share how distributed Tensorflow training works, various options for distributed training, and when to choose what option. We’ll also share some best practices on using distributed Tensorflow on top of Kubernetes, based on our latest performance tests performed on public cloud providers. All work presented in this session will be accessible via a public Github repository.
Speakers
avatar for Lachie Evenson

Lachie Evenson

Principal Program Manager, Microsoft
Lachlan is a Principal Program Manager on the open source team at Azure. As a cloud native ambassador, emeritus Kubernetes steering committee member and release lead, Lachlan has deep operational knowledge of many Cloud Native projects. He spends his days building and contributing... Read More →
avatar for Joy Qiao

Joy Qiao

Senior Solution Architect - AI and Research Group, Microsoft
Joy Qiao is a senior solution architect in the AI & Research Group at Microsoft, where she is responsible for driving end-to-end AI/ML solutions on Azure among the partner eco-system. Joy has over 15 years of IT industry experience including 11 years at Microsoft working as technical... Read More →

Democratizing Machine Learning on Kubernetes pdf
Friday December 8, 2017 2:45pm - 3:20pm CST
Meeting Room 9C, Level 3
  Data + Machine Learning - KubeCon