Honeywell: Delivering on the Power of Outlier Detection

Data Science Leaders

Every organization has an abundance of outlier detection use cases, but how do you turn them into repeatable, scalable AI products that drive a virtuous cycle of adoption and impact?
To answer this question, Jan Zirnstein, Senior Data Science Director at Honeywell,. shares their best practices for successfully driving value using anomaly detection, how to build trust with stakeholders, and the importance of both product management and software development resources.
Join us as we discuss:

• How to spark a virtuous cycle with anomaly detection use cases
• Driving continuous improvement by transitioning from unsupervised to supervised machine learning
• Aligning the model development and software development lifecycles