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Webinar - Enrich Tableau with ML - Schindlauer (20160615)

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Presented by Roman Schindlauer

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Webinar - Enrich Tableau with ML - Schindlauer (20160615)

  1. 1. Tableau & Dato Predictive Services Machine Learning in Tableau with Python
  2. 2. 2 Hello my name is.. Roman Schindlauer Senior Product Manager, Dato
  3. 3. 3 Hello my name is.. Bora Beran Staff Product Manager, Tableau
  4. 4. 4 Agenda • Overview • Demos: - Clustering - Sentiment Analysis - Lead Scoring • Best Practices
  5. 5. 5 Dato & Tableau • The power of Python in Tableau dashboards • Tableau 8.1: R • Tableau 10: Python • Scenarios: - Run complex models on user interaction data (churn prediction, lead scoring, sentiment analysis, risk analysis) - Process workbook data through an expressive programming language - Share external functions and models
  6. 6. Clustering with scikit-learn
  7. 7. 7 Dato Predictive Services • Machine Learning in production Load Balancer Application Python code Model queries Model Server Model Server Metrics Logs Cache Python execution environment Model server Management models, ops
  8. 8. 8 Predictive Service Tableau Integration • Execute code in a PS Tableau SCRIPT( ... ) Python execution environment ML modelML modelML model
  9. 9. Sentiment Analysis with GraphLab Create
  10. 10. 10 PS as a Model Server • Persist code in the server, call like a function • Discover, share and re-use • Update without affecting clients Predictive Service Tableau SCRIPT( ... ) Python execution environment ML modelML modelML model
  11. 11. Deploying a trained model
  12. 12. 12 Discovering deployed models • Workbook Dato Model Catalog: - Uses a WDC that talks to a PS - Lists deployed models - Ideally, model author has added metadata to the service - Simplifies the SCRIPT authoring
  13. 13. 13 Service Management & Monitoring • Connect: ps = gl.deploy.predictive_service.load(“s3://…”) • Status: ps.get_status() • Scale the service: ps.add_nodes(1) ps.set_scale_factor(4) • Get metrics: ps.get_metrics(name="test", start_time=“6h”) Need path and access to state file!
  14. 14. 14 Summary • ML integration - Execute any Python code - Call deployed models (Discover through WDC-based catalog) - Data type compatibility • Efficiency - Define correct table calculations - Scale the predictive service - Monitor PS health • Security - Querying: API key is auto-generated, can be changed - Administration: PS admins need access to state path - SSL is supported
  15. 15. 15 Further sources • http://www.tableau.com/support/desktop • https://dato.com/learn/ - Notebook gallery - User guide - API reference • Dato Webinars
  16. 16. ● 2 days with 60 talks and hands-on tutorials ● Tools, techniques, latest research & best practices ● Speakers from Pinterest, Pandora, Kaggle, Google, UW, Quora, Salesforce, Uber, Tableau, Stanford, CMU and more Data Science Summit Register Now http://bit.ly/DSS-SF-2016
  17. 17. Questions?
  18. 18. Thank you for joining us!
  • josephhodges560

    Jul. 19, 2020

Presented by Roman Schindlauer

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