Deploy AutoML with Model Explainability

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In collaboration with Women Who Code Taipei, a non-profit organization in Taiwan, TIBCO introduces how to deploy autoML with model explainability.

Machine learning models play a vital role in business problems such as fraud detection, customer churn, predictive maintenance, and many others. See how automated machine learning (autoML) can jump start the process while still providing transparency using model explainability. We provide a tour of our latest autoML techniques, including use of text variables with explanations of how they affect your models.  

Deploy AutoML with Model Explainability
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Speakers

Jingchuan Lin

Jingchuan Lin

Regional Data Scientist
TIBCO Software

Success with TIBCO:

3 vs. 10-12

Months to develop a real-time system with TIBCO Streaming vs. open source

1

centralized source of information, down from 10

22 PERCENT INCREASE

In revenues via price optimization, cross-sell, and fraud detection

74 Percent

Fewer surgical site infections
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