Beyond Prediction Series: When a prediction is not enough – The “decision gap” when using ML and AI in the real world
The remarkable and rapid development of AI and Machine Learning techniques for supervised learning has generated a high level of interest in applying algorithms that enable companies to solve many use-cases.
A classic example for AI/ML is churn modeling across many industries, where good models can be built for predicting customer churn. But real business impact is more complicated. Sometimes, the naïve approach of intervening with high churn probability customers can actually make them more likely to churn. We demonstrate a systematic framework for avoiding such pitfalls based on decision science and experimental design.
- Progress in Supervised Machine Learning
- Real-world business cases and Application of AI/ML techniques in
- Medical policy
- Advertising ROI
- Customer retention
- Introduction to AWS AI and ML Services, frameworks and infrastructure
Dr. Rajan Lukose, Chief Data Scientist, Cambridge Technology, Inc.
Nitin Tyagi, Vice President, Enterprise Solutions, Cambridge Technology, Inc.