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 a 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/