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Thursday, Aug. 29, 2019 | 10:00 AM - 11:00 AM UTC+1
Before Kubernetes, gaps between Machine Leaning engineers and Platform Technology engineers exist, as two domain required different skill sets. Thanks to Kubernetes, new trend such as AIOps, reduces the gap and makes data scientist can efficiently complete their work and excellence, even for a production purpose. In this session, we will discuss the latest Kubernetes development for data science, such as Kubeflow and arena. And then, demonstrate setting up K8s managed clusters, deploying Jupyter Notebook data science environment, training machine learning model in a distributed running environment and deploying prediction application in cloud-native.
Miss the first webinar? Check it out here:
k8s webinar 1: https://resource.alibabacloud.com/webinar/detail.html?id=946
•Demo to train a machine learning model in Kubernetes with distributing GPUs
•Understand machine learning challenges, Kubernetes, Kubeflow, and arena
•Demo to deploy a machine learning model in Kubernetes
•MLOps trend discussion
Dr. Jianhua Shao is a data science solution architect in Alibaba Cloud. Although London based, he works to design and deliver Alibaba cloud solution for Alibaba global customers, with a primary focus in data, AI and finance. Before Alibaba, he worked as a data scientist in Barclays Bank (London), T-Mobile (Berlin), China Mobile (Beijing), Cass Business School (London), University College Dublin (Ireland) and several startups. He has PhD degree in digital economy and BSc degree in CS.
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