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Fri, December 4 at 5:30 AM - 9:30 AM GMT+5:30DeepTech DataTech
With the advent of containers, Kubernetes evolved as the defacto orchestration solution to coordinate hundreds of containers at scale across a datacenter. Kubernetes opens the door for developers to access the benefits of distributed computing. As compute capacity increases relative to price we have an explosion of Machine Learning applications moving to Kubernetes. Does anybody remember “Wonder Twin powers, activate!”
You will learn how Kubernetes offers to Machine Learning an ideal orchestration tool for hosting your clever applications. We will look at common practices, containers, and deployment architectures that are common for cloud native Machine Learning. Kubeflow is one of the dominating solutions, but there are others.
Hands-on exercises:
Prerequisites:
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Jonathan is an independent software architect with a concentration on helping others unpack the riches in the cloud native and Kubernetes ecosystems.
Jonathan is halfway into his second score of engineering commercial software, driven by his desire to design helpful software to move us forward. His applications began with laboratory instrument software and managing its data. Jonathan was enticed by the advent of object-oriented design to develop personal banking software. Banking soon turned to the internet, and enterprise applications took off. Java exploded onto the scene, and since then he has inhabited that ecosystem. At 454 Life Sciences and Roche Diagnostics, Jonathan returned to laboratory software and leveraged Java-based state machines and enterprise services to manage the terabytes of data flowing out of DNA sequencing instruments. Then as a hands-on architect at Thermo Fisher Scientific, he applied the advantages of microservices, containers, and Kubernetes to their laboratory management platform.
