ARIZE MACHINE LEARNING COURSE

The Fundamentals

• ML Observability Course Overview
• What Is Observability?
  • ML Observability: The Essentials
  • Tracing In Machine Learning
• Model Evaluation Metrics
  • Binary Cross Entropy
  • Precision
  • Recall
  • Calibration Curve
  • PR AUC
  • AUC ROC
  • Mean Absolute Percentage Error (MAPE)
  • Normalized Discounted Cumulative Gain (NDCG)
• Drift Metrics
  • Data Binning
  • Population Stability Index (PSI)
  • KL Divergence
  • Jensen Shannon Divergence
  • Kolmogorov Smirnov Test
• Fairness & Bias Metrics
• Data Quality
• Service Monitoring Metrics
  • ML Service-Level Performance Monitoring Essentials
• Explainability
  • Explainability Techniques
• Monitoring Image and Language Models and Embeddings
  • KNN Algorithm
  • Tokenization
  • Embedding Versioning
  • Dimensionality Reduction
  • Monitoring Embedding/Vector Drift
  • BERT
  • Bleu Score and Other Large Language Model Metrics

• Certifications
  • Arize University
• New to Arize?
  • Arize onboarding
• Glossary
  • See all terms