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Kubernetes, AI Gateways, and the Future of MLOps // Alexa Griffith // #294

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Navigating Airflow and Emerging Workflow Tools

This chapter explores the speakers' experiences with Airflow in data science, discussing challenges like system crashes and workload management. They reflect on the evolution of workflow tools, comparing Airflow with newer solutions such as CoopFlow and Argo, while emphasizing the diversity of user needs across DevOps, data engineering, and ML engineering. The conversation also highlights the importance of user-friendly interfaces in complex systems like Kubernetes to bridge the gap between technical and non-technical users.

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