Deploy AutoML with Model Explainability

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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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

3 Months

Time to learn Spotfire and develop the first analysis model

74 Percent

Fewer surgical site infections

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