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Building Smarter Pipelines for AI, Maps, and ML with Airflow
Manage episode 496321960 series 3646168
Matt Forrest에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Matt Forrest 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
If you’re working with spatial data, AI workflows, or massive batch jobs, you’ve probably hacked together more than a few pipelines. But what if there’s a better way?
In this episode, I sit down with Kenten Danas, Senior Manager of Developer Relations at @Astronomer to explore how Apache Airflow powers the modern data stack including real-world geospatial and climate risk modeling pipelines.
We cover:
What Airflow actually is (and why it’s everywhere)
How it’s used in geospatial pipelines, AI, and LLM workflows
New features in Airflow 3.0 like assets, remote execution, and backfills
Why orchestration is the key to scalable spatial data processing
Tools like the Airflow AI SDK that make LLM pipelines easier to manage
Links from the show:
Astronomer Academy (with courses + certifications): https://academy.astronomer.io/
Astronomer Webinars: https://www.astronomer.io/events/webi...
Astro CLI (for running Airflow locally): https://www.astronomer.io/docs/astro/...
Free trial of Astro: https://www.astronomer.io/lp/signup/
Airflow AI SDK (open source Python SDK for working with LLMs from Airflow): https://github.com/astronomer/airflow...
Vibrant Planet Geospatial + ML Airflow use case: https://www.astronomer.io/blog/airflo...
Whether you're building spatial features for machine learning or just want a more reliable way to manage your data workflows this is the episode for you.
29 에피소드
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