Bridging the gap between AI and KPIs
Sinan Ozdemir
Director of Data Science
Directly
Many companies silo data science and machine learning teams who optimize AI metrics like precision, F1 and accuracy away from teams focused on optimizing KPIs such as revenue and churn. This gap can lead to models being put into production that have no obvious business goal. It is imperative that business leaders understand how to close this gap and have machine learning engineers working with non-engineering teams in order to build models that fit into the business' KPI goals.
Interested in Structuring?
Visit our Structuring community!
How should an ideal organization look like? What approaches can help you to make your organization more efficient? Let's try to find answers together in our Structuring community. Here we discuss topics of org design/team structure, process (agile/kanban, etc), engineering metrics, knowledge management/documentation.
VIDEOS RELATED TO STRUCTURING
Bryan Helmkamp, Founder and CEO at Code Climate
Brian guthrie, Co-Founder and CTO at Orgspace
Denise Iglesias, EVP, Product and Engineering at Dealerware
Mona Soni, CTO at Sustainable1 at S&P
Randy Shoup, VP Engineering and Chief Architect at eBay
Johanna Rothman, Owner at Rothman Consulting Group
Liz Keogh, Lean/agile consultant at Lunivore
Ashkan Roshanayi, Founder at DataChef - Ex Amazonian at DataChef
Neville Samuell, VP of Engineering at Ethyca
Colleen Tartow, Director of Engineering at Starburst Data
Emily Nakashima, VP Engineering at Honeycomb.io

Copyright © 2022 CTO Connection, All Rights Reserved