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apidays LIVE JAKARTA - Machine Learning powered API governance by Jenks Guo

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apidays LIVE JAKARTA - Connecting the Digital Stack
Machine Learning powered API governance
Jenks Guo, Developer Evangelist at Xero

Published in: Technology
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apidays LIVE JAKARTA - Machine Learning powered API governance by Jenks Guo

  1. 1. Machine Learning Powered API Governance Jenks Guo
  2. 2. The era of platform businesses Softwares are transforming to platforms Growing partners means growing APIs Partners 📈 issues 📈 governance 📉
  3. 3. Monitoring the API platform Logging Alarms & Alerts Usage reports API security tools
  4. 4. A hidden problem in all API platforms Partners pivot in use cases of APIs can cause: ● Lengthy investigation ● Long engagements ● Tough negotiation ● Shutting down of API accesses
  5. 5. Poorly governed vs. well governed Poorly governed platforms: ● End users’ complaints ● Partners’ self-reporting/self-policing ● Just wait for incidents to happen Well governed platforms: ● Engineers audit ● Sales / partnership team check in ● Mandated self-reporting for new use cases Illustrations by Jean Wei
  6. 6. First experts, then expert systems Who’s your go-to API expert? What problem are they solving? Illustration by Andrew Rae; Photograph: Bettmann/Corbis
  7. 7. What kind of problem is this? Anomaly detection? Categorisation!
  8. 8. Clustering analysis Unsupervised learning How similar are datasets Metadata of APIs Human needs to tweak
  9. 9. API Partner POST Contact GET Contact POST Invoice GET Invoice POST Inventory GET Inventory Partner A 30 3 20 23 0 0 Partner B 29 7 45 60 0 0 Partner C 0 30 1 15 0 0 Partner D 3 50 2 23 0 0 Partner E 0 40 3 56 0 0 Partner F 0 0 98 23 20 45 Partner G 0 0 76 34 67 89 API usage data
  10. 10. API usage profile
  11. 11. Step 1 - Analyse in 2D
  12. 12. Step 2 - Analyse in 3D
  13. 13. Next steps - more dimensions & repeat
  14. 14. The End Result API Partners Group ID A 1 B 1 C 0 D 0 E 0 F 2 G 2 API Partners Group ID A 3 B 1 C 0 D 0 E 0 F 2 G 3
  15. 15. Connectivity models Centroid models Distribution models Subspace models Group models Graph-based models Neural models Hard clustering Soft clustering Strict partitioning clustering Overlapping clustering Hierarchical clustering Subspace clustering
  16. 16. Summary Illustrations by Jean Wei
  17. 17. Q & A

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