Machine learning can be applied to innovate products, automate processes, and improve customer experiences. While AI is often hyped as a replacement for people, in reality, ML models can get confused by exceptions, edge cases, and unusual data, and when that happens, a person — sometimes called a human in the loop (HITL) — must get involved.
Watch a replay of our discussion with Dean Abbott, Chief Data Scientist at SmarterHQ, and learn:
Why it pays to have people in the loop during ML model deployment
Tips for deploying people more strategically to ease ML development challenges
How to design a HITL approach for better model outcomes
WATCH THE WEBINAR
Tell us about yourself
Keith McCormick
PRESENTER
Keith is CloudFactory’s Chief Data Science Advisor. He’s also an author, LinkedIn Learning contributor, university instructor, and conference speaker.
Dean Abbott
PRESENTER
Dean Abbott is Co-Founder and Chief Data Scientist of SmarterHQ (a Wunderkind Company) and President of Abbott Analytics. He wrote the book Applied Predictive Analytics. He has a bachelor’s degree in the mathematics of computation from Rensselaer and a master’s degree in applied mathematics from the University of Virginia.
