Model Evaluation & Metrics – Measure What Matters

30 Days of MLOps Challenge: Day 8

By Sandip Das · · 8 min read

Model Evaluation & Metrics – Measure What Matters
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Hey — It's Sandip Das 👋

We must understand model evaluation and metrics to ensure deployed models perform reliably in real-world scenarios.

By measuring what matters—like accuracy, precision, recall, and AUC—they can monitor drift, improve models, and align outcomes with business goals.
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Previous => Day 7: Model Experiment Tracking with MLflow – Log It or Lose It

📚 Key Learnings

  • Why evaluation metrics are critical to assess ML model performance
  • Core classification metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC, Confusion Matrix
  • Difference between class

    Read the full article on learnxops.com.

    Topics: MLOps, ML Training, Community, 30 Days of MLOps