Coffee Sessions #109

Why You Need More Than Airflow

Airflow is a beloved tool by data engineers and Machine Learning Engineers alike. But when doing ML what are the shortcomings and why is an orchestration tool like that not always the best developer experience? In this episode, we break down what some key drivers are for using an ML-specific orchestration tool.

Take-aways

Why do we need a fabric for ML orchestration? How do you build this layer correctly?

In this episode

Ketan Umare

Ketan Umare

Co-Founder and CEO, Union.ai

Ketan Umare is the CEO and co-founder at Union.ai. Previously he had multiple Senior roles at Lyft, Oracle, and Amazon ranging from Cloud, Distributed storage, Mapping (map-making), and machine-learning systems. He is passionate about building software that makes engineers' lives easier and provides simplified access to large-scale systems. Besides software, he is a proud father, husband, and enjoys traveling and outdoor activities.

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Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

George Pearse

George Pearse

Host

George attained his Bachelors in Physics from the University of Birmingham in 2019, his final year project in Medical Imaging investigated a low radiation alternative to Gamma Cameras for use in tumour imaging. George believes advances in AI will lead to improvements in the quality and speed of healthcare, freeing up resources for complex cases and preventative medicine in the process. He will be working within our Research and Development team focusing on the key areas of clinical data and increasing the efficiency of research as Behold.ai continues to innovate.