Youness ECHCHADI

Youness ECHCHADI

Cloud & Solution Architect
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  • Avoiding Data Leakage with Scikit-learn Pipelines
  • Making Data Pipelines Reproducible: Version Inputs, Code, and Parameters
  • AWS IAM Roles for Automation: A Practical Least-Privilege Design
  • AWS Cost Alerts Are Not a Kill Switch: Designing a Runaway-Workload Containment Loop
  • Mastering Bioinformatics Pipeline Automation: From Scripts to Cloud-Native Workflows

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Tag: Data Engineering

Making Data Pipelines Reproducible: Version Inputs, Code, and Parameters
Data Science

Making Data Pipelines Reproducible: Version Inputs, Code, and Parameters

Posted on October 6, 2026by Youness ECHCHADI

When a pipeline produces an unexpected result, the first questions are usually simple: Which data did it read? Which code ran? What parameters were used? If the answers are scattered across logs, shell history, and mutable object paths, reproducing the run becomes guesswork.

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