A comprehensive guide on building and maintaining scalable AI workflows using Apache Airflow, covering everything from basic setup to advanced topics like security, testing, and monitoring. The articl
e provides practical code examples and best practices for creating modular, maintainable, and efficient data pipelines for AI applications.
Reasons to Read -- Learn:
how to build production-grade AI pipelines using Apache Airflow, with practical code examples for implementing DAGs, error handling, and cloud integration with AWS.
essential security best practices for AI workflows, including proper handling of sensitive credentials using AWS Secrets Manager and implementing role-based access control.
specific optimization techniques for scaling AI pipelines, including how to configure task parallelism in Airflow and integrate with Kubernetes for improved performance.
publisher: Best coding practices – AI, Python, data bases, cloud computing, work flow and so on
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