CIO’s considerations while acquiring AI/ML Capabilities
by Chandra Pendyala CIOs are contending with a lot of technology buzz words. In this article I intend to convert the discussion about Data Science and Machine Learning into a business discussion. There are some important differences between the usual technology acquisition programs and these technologies. I will address those differences and propose some upgrades […]
Is OR from Mars and ML from Venus?
by Chandra Pendyala How do we model the real world in computers? How do we solve real world problems with computers? Math is neat, it captures the truth without any of the unnecessary ugliness. Most importantly it gives us millennia of accumulated tools to make sense out of a model of reality. But is it […]
A VP’s check list for monitoring AI/ML projects
by Chandra Pendyala Enthusiastic engineers are cranking out quick AI/ML prototypes at great speed. This check list tries to help VPs shepherd this energy into valuable solutions for the business. Business Value Promise: Traditional project gating and prioritizing methods work here. The only upgrade needed for traditional methods is in the computation of life time costs […]
Build Differentiated Innovation- AI/ML
by Chandra Pendyala Mission critical AI-ML systems need interpretable, traceability, auditing, bias detection, security, compliance, governance, ownership and monitoring. Highly interpretable systems do not feel like they are automating learning and intelligence. Completely uninterpretable systems without proper effectiveness metrics will feel like random numbers generating black-boxes. Natural Language Processing, Image Processing, Recommendation Engines, Content Generating […]