How Airflow is using AI to make data engineering more resilient, not more complex
The post explains how Airflow is adding AI-assisted reliability features that detect schema drift, resume long-running jobs from saved state, and classify failures using team runbooks. It shows AI being used inside data infrastructure rather than as an app layer, making pipelines more self-healing without forcing data engineers to rebuild their workflows around agents.
https://blog.dataengineerthings.org/how-airflow-is-using-ai-to-make-data-engineering-more-resilient-not-more-complex-36ff44fd8df7
The post explains how Airflow is adding AI-assisted reliability features that detect schema drift, resume long-running jobs from saved state, and classify failures using team runbooks. It shows AI being used inside data infrastructure rather than as an app layer, making pipelines more self-healing without forcing data engineers to rebuild their workflows around agents.
https://blog.dataengineerthings.org/how-airflow-is-using-ai-to-make-data-engineering-more-resilient-not-more-complex-36ff44fd8df7
Medium
How Airflow is using AI to make data engineering more resilient, not more complex
Your pipeline failed at 2am. What if it could fix itself?
huggingface / speech-to-speech
Build local voice agents with open-source models
https://github.com/huggingface/speech-to-speech
Build local voice agents with open-source models
https://github.com/huggingface/speech-to-speech
GitHub
GitHub - huggingface/speech-to-speech: Build local voice agents with open-source models
Build local voice agents with open-source models. Contribute to huggingface/speech-to-speech development by creating an account on GitHub.