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Secrets Management for ML Pipelines
ML pipelines handle more credentials than most code, and leak them in places ordinary software does not, such as experiment trackers. This guide plants AWS's published example key in a project and follows it: a scanner flags it, MLflow stores it in plain text, a log filter redacts it and a vault keeps it out.
Environment Variables and Secrets in Python Apps
A practical setup for configuration and secrets in Python applications: reading typed settings from environment variables and failing fast when one is missing, .env files that never reach Git, keeping secrets out of logs and Docker images, and what to do when a key leaks anyway.