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Applying DevOps Practices in Data and ML Engineering

Posted Oct 26
# Versatile Data Kit
# DevOps Practices
# Data Engineering
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SPEAKER
Antoni Ivanov
Antoni Ivanov
Antoni Ivanov
Software Engineer @ VMWare

Antoni Ivanov is a Software Engineer specializing in scalable big data systems and data analytics infrastructure. Antoni has been working on building VMware data analytics platform from its beginning.

Antoni is a lead maintainer of the recently open-sourced project Versatile Data Kit. Versatile Data Kit has transformed data engineering at VMware towards being code-first, fully automated, and decentralized. Now Antoni is working to bring that as open source software to all data practitioners in the community.

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Antoni Ivanov is a Software Engineer specializing in scalable big data systems and data analytics infrastructure. Antoni has been working on building VMware data analytics platform from its beginning.

Antoni is a lead maintainer of the recently open-sourced project Versatile Data Kit. Versatile Data Kit has transformed data engineering at VMware towards being code-first, fully automated, and decentralized. Now Antoni is working to bring that as open source software to all data practitioners in the community.

+ Read More
SUMMARY

Modern Data and ML engineering needs to be agile and able to quickly respond to a changing business landscape without sacrificing necessary data quality.

DevOps revolutionized Software engineering with its adoption of agile, lean practices, and fostering collaboration. We can see the same need to happen for Data Engineering as well.

We will go over how we can adopt the best DevOps practices in the space of data engineering. And what are the challenges in adopting them considering the different skill sets of the data engineers and the different needs?

We will show and demonstrate together how a new open-source project Versatile Data Kit (https://github.com/vmware/versatile-data-kit) answers those questions. We will create an end-to-end data pipeline, and productionize it quickly and efficiently.

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