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Anomaly Detection for Global Scale at Netflix

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How Netflix developed anomaly detection algorithm which has been applied in multiple contexts
Robust to prior anomalies
Handle high cardinality dimensions
Handles seasonality
Handle data which is not always normally distributed
Challenge - more anomalies than we can handle from a human perspective

Published in: Data & Analytics
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Anomaly Detection for Global Scale at Netflix

  1. 1. GLOBAL SCALE ANOMALY DETECTION FOR
  2. 2. DIRECTOR, DATA ENGINEERING & ANALYTICS PAUL ELLWOOD
  3. 3. 1 INTERNATIONAL EXPANSION
  4. 4. 2 FINITE ANALYST CAPACITY
  5. 5. UNREALISTIC EXPECTATIONS TEST ANALYSIS DASHBOARD ▸ One test ▸ One report (of the five) ▸ Ignoring multi-select combinations ▸ Ignoring date range selections
  6. 6. 16,821,043,200 UNIQUE VIEWS
  7. 7. 3 ANOMALY DETECTION
  8. 8. SMARTER ANALYTIC PRODUCTS
  9. 9. SMARTER ANALYTIC PRODUCTS ▸ Statistical Anomalies ▸ Personalized ▸ Filtered & Ranked by Impact ▸ Usable ▸ Ability to Dig Deeper
  10. 10. CASE STUDY PAYMENT PROCESSING ▸ Multiple dimensions ▸ High cardinality dimensions ▸ Hundreds of thousands of time series ▸ Increasing volumes ▸ High business impact
  11. 11. QUESTIONS?
  12. 12. DIRECTOR, DATA ENGINEERING & ANALYTICS PAUL ELLWOOD @pellwood https://www.linkedin.com/in/pellwood

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