MLOps Coffee Sessions #5 // Airflow in MLOps // Featuring Simon Darr and Byron Allen

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 ||

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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.

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Timestamps:
[00:00] Introduction to Simon, Byron, and the sponsor, Servian
[03:05] Accelerate ML framework
[03:45] ML maturity assessment
[04:20] ML test score
[05:28] Overview of Airflow
[09:13] Using Airflow in ML
[12:43] Misconceptions on Airflow
[14:45] Architecture of Airflow
[18:35] Airflow demonstration
[30:50] Transparency of Airflow
[32:06] Why logging ability matters?
[40:30] Downsides of Airflow
[47:42] Final thoughts on Airflow
[52:07] Wrap up