Applying DevOps Practices in Data and ML Engineering

Name: Applying%20DevOps%20Practices%20in%20Data%20and%20ML%20Engineering
Uploaded: 2022-10-26

Posted Oct 26, 2022 | Views 726

# Versatile Data Kit

# DevOps Practices

# Data Engineering

# VMWare

# VMWare.com

speakers

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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Demetrios Brinkmann

Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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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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