Explainable AI (XAI) in Production – SHAP, LIME, and Interpretability Techniques
30 Days of MLOps Challenge: Day 18
By Sandip Das · · 8 min read
💡
Hey — It's Sandip Das 👋
We should learn Explainable AI (XAI) to ensure transparency, trust, and accountability in model predictions, especially in regulated or high-stakes domains.
Tools like SHAP and LIME help diagnose model behavior, debug errors, and support compliance by making black-box models interpretable.
We should learn Explainable AI (XAI) to ensure transparency, trust, and accountability in model predictions, especially in regulated or high-stakes domains.
Tools like SHAP and LIME help diagnose model behavior, debug errors, and support compliance by making black-box models interpretable.
📚 Key Learnings
- Understand Explainable AI (XAI)
- Learn how SHAP and LIME provide insights into model predictions
- Integrate XAI into production APIs, dashboards, or monitoring flows
- Support compliance, debugging, and trust using interpretable outputs
- Understand the importance of model interpretability in real-world ML deployments
🧠 Learn here
Explainable AI (XAI)
Read the full article on learnxops.com.
Topics: MLOps, Community, XAI, 30 Days of MLOps