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
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
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