Democratizing Machine Learning on Kubernetes [I] - Joy Qiao & Lachlan Evenson, Microsoft
Channel: CNCF [Cloud Native Computing Foundation]
Date: 15/12/2017

Democratizing Machine Learning on Kubernetes [I] - Joy Qiao & Lachlan Evenson, Microsoft

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.

About Lachlan Evenson
Lachlan Evenson is a cloud native evangelist and mercenary. Lachlan has spent the last two and a half years working with Kubernetes and enabling cloud native journeys. He is a believer in open source and is an active community member. Lachlan spends his days helping make cloud native projects run great on Azure.

About Joy Qiao
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 lead/architect roles at various Azure & AI engineering teams, as well as senior consultant/architect in the Microsoft services team. Joy has mainly been focusing on Microsoft Azure, Big Data and Machine Learning technologies, leading and delivering Machine Learning, Big Data and Cloud-based solutions for both internal and external MS enterprise customers and partners.
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