ML Model Governance & Compliance – Auditing, Explainability & Fairness in ML
30 Days of MLOps Challenge: Day 17
By Sandip Das · · 7 min read
💡
Hey — It's Sandip Das 👋
We should learn model governance and compliance to ensure accountability, traceability, and fairness in machine learning systems, especially when handling sensitive or regulated data.
Mastering auditing, explainability, and bias detection helps build trustworthy and compliant AI solutions that align with legal and ethical standards.
We should learn model governance and compliance to ensure accountability, traceability, and fairness in machine learning systems, especially when handling sensitive or regulated data.
Mastering auditing, explainability, and bias detection helps build trustworthy and compliant AI solutions that align with legal and ethical standards.
📚 Key Learnings
- Understand the importance of governance in ML systems: accountability, traceability, compliance
- Learn how to audit model lineage and maintain version history
- Explore explainability tools (e.g., SHAP, LIME) to interpret black-box models
- Identify and address bias and fairness concerns in datasets and predictions
- Study compliance frameworks: GDPR, HIPAA, SOC2 as they relate to ML
🧠 Learn h
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Topics: MLOps, DevSecOps, Community, 30 Days of MLOps