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Airflow is a renowned tool for data engineering. It helps with orchestrating ETL workloads, and it's well-regarded amongst machine learning engineers as well. So, how does Airflow work, and how is it applied to MLOps?

In this episode, Demetrios and David are joined by Simon Darr, a Managing Consultant at Servian, with many years of experience using Airflow, along with Byron Allen, a Senior Consultant at Servian, specializing in ML. The group discusses how Airflow works, its pros and cons for MLOps, and how it is used in practice, along with a short demo.

|| Links Referenced in the Show ||
Maxime Beauchemin on Medium https://medium.com/@maximebeauchemin
The Rise of the Data Engineer: https://www.freecodecamp.org/news/the-rise-of-the-data-engineer-91be18f1e603/
Using Airflow with Kubernetes at Benevolent AI: https://www.benevolent.com/engineering-blog/using-airflow-with-kubernetes-at-benevolentai

|| Sponsored Content ||
Servian is a global data consultancy, providing advisory and delivery for data engineering and ML/AI projects. Accelerate ML is their framework to streamline and maximize the impact of ML workflows on an organization. As a part of that framework, they have a free tool used to help clients understand ML maturity. Check out the framework here, along with the ML maturity assessment.

Accelerate ML framework: https://www.servian.com/accelerate-ml/
ML Maturity Assessment: https://forms.gle/4ZN9htWjSUsSBkfd7

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Simon on LinkedIn: https://www.linkedin.com/in/sdarr/
Connect with Byron on LinkedIn: https://www.linkedin.com/in/byronaallen/