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Scaling the Data Scientist

Scaling the Data Scientist. Dr. Ira Cohen, Chief Data Scientist, HP Software. HP-Software and Data Science. HP-Software products collect huge amounts of IT data. Requirements. Changes. Defects. Security events. System Monitoring. Logs.

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Scaling the Data Scientist

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  1. Scaling the Data Scientist Dr. Ira Cohen, Chief Data Scientist, HP Software

  2. HP-Software and Data Science HP-Software products collect huge amounts of IT data Requirements Changes Defects Security events System Monitoring Logs • “Big Data & Predictive Analytics: The Future of IT Management” MikeGualtieri, Forrester  Events Configuration Incidents Network data App Monitoring Test data Customers want us to transform the data to actionable information

  3. Need

  4. A tale of two worlds

  5. Our solution Developer Data analytics specialist

  6. How? Data infrastructure New Dev tool Training Mentoring Community

  7. Training: Practical Machine Learning 4 day training Commitment to complete first project

  8. Early detection of anomalous behavior in IT systems Yonatan Ben Simhon & Yaneeve Shekel Practical Machine LearningOhad Assulin, Efrat Egozi Levi, Ira Cohen Automatic Vulnerability Categorization Barak Raz & Ben Feher Sales Pipeline Early Warning Gabriel, Alvarado Classifying Security Events Yoni Roit & Omer Weissman Predictive Analytics in Release Management Sigalit Sade URL to Action Classification Boaz Shor & Eyal Kenigsberg Cloud Delivery Optimization (CDO) Ran, Levi Automatic Event Prioritization Anat Levinger & Roy Wallerstein

  9. Pushing My Buttons Gil Zieder, Ofer Eliassaf, Boris Kozorovitzky

  10. The process @ work As a Pusher or DevOps of a project you would like to know if the given change set is safe to push into the production branch. Rank based attribute selection 87% Accuracy with K-NN • 9 open source projects, 8806 individual commits • Get labels of “good” or “bad” commit by running tests after each commit • “good” – tests pass, “bad” – tests fail • Classification algorithms • K-NN, SVM, Decision Tree, Random Forest, … • 80attributes per commit • source control, previous commits, and code complexity based attributes: • e.g., average change frequency, previous commit state, cyclomatic complexity

  11. Analytic specialist program: Results

  12. Can we do better? • Yes. From months to days! • How? • Create a simple tool for analytic specialists • Automate the data scientist as much as possible

  13. Project Titan

  14. Titan: Demo

  15. Scaling the data scientist Data Scientist • Provides expert advice • Develops new types of machine learning solutions Analytic specialists Develops using standard machine learning Uses simplified tool

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