Why data scientists shouldn't need to know about Kubernetes
ON DEMAND | Originally Aired May 24, 2022
Data scientists should be focused on innovation and business impact, not DevOps
Kubernetes is great for handling bursty data science workloads, providing the infrastructure abstraction and compartmentalization required to manage the data science lifecycle.
But do data scientists need to be “full stack” experts with deep understanding of kubernetes and DevOps?
No. Bringing Domino and VMware Tanzu together makes it easier for researchers and data scientists to access the compute resources they need to deliver business impact.
Download the recording to hear from experts from VMware and Domino to learn why and how a modern IT stack can get your data scientists focused on…data science instead of DevOps challenges. You'll see:
- How Domino’s Enterprise MLOps platform can be deployed on VMware Tanzu, and...
- A demo of a powerful data science use case.
Meet the speakers
Nikolay Manchev is the Principal Data Scientist for EMEA at Domino Data Lab.
In this role, Nikolay helps clients from a wide range of industries tackle challenging machine learning use-cases and successfully integrate predictive analytics in their domain specific workflows. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King's College London. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks and computational neurobiology.
Sridhar Vennela is a Senior Member of Technical Staff at VMware.
As a kubernetes community member and Certified Kubernetes Associate (CKA), he works with ISV partners to integrate the software with Kubernetes. Recently, he started working on Cloud Native Buildpacks (CNBs) to help security partners run scanning software in containers. He has published Kubernetes oriented blogs on Medium and organizes workshops for partners.
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