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Tableau &
Dato Predictive
Services
Machine Learning in Tableau with Python
2
Hello my name is..
Roman Schindlauer
Senior Product Manager, Dato
3
Hello my name is..
Bora Beran
Staff Product Manager, Tableau
4
Agenda
• Overview
• Demos:
- Clustering
- Sentiment Analysis
- Lead Scoring
• Best Practices
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
Clustering with scikit-learn
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
Predictive Service
Tableau Integration
• Execute code in a PS
Tableau
SCRIPT(
...
)
Python execution environment
ML modelML modelML model
Sentiment Analysis with GraphLab Create
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
Deploying a trained model
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
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
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
Further sources
• http://www.tableau.com/support/desktop
• https://dato.com/learn/
- Notebook gallery
- User guide
- API reference
• Dato Webinars
● 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
Questions?
Thank you for joining us!

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Webinar - Enrich Tableau with ML - Schindlauer (20160615)

  • 1. Tableau & Dato Predictive Services Machine Learning in Tableau with Python
  • 2. 2 Hello my name is.. Roman Schindlauer Senior Product Manager, Dato
  • 3. 3 Hello my name is.. Bora Beran Staff Product Manager, Tableau
  • 4. 4 Agenda • Overview • Demos: - Clustering - Sentiment Analysis - Lead Scoring • Best Practices
  • 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
  • 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 Predictive Service Tableau Integration • Execute code in a PS Tableau SCRIPT( ... ) Python execution environment ML modelML modelML model
  • 9. Sentiment Analysis with GraphLab Create
  • 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
  • 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 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 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 Further sources • http://www.tableau.com/support/desktop • https://dato.com/learn/ - Notebook gallery - User guide - API reference • Dato Webinars
  • 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
  • 18. Thank you for joining us!