T. Scott Clendaniel on LinkedIn: #LinkedIn #AI #DataScience

Building a High-Performance Data and AI Organization is a great read this weekend if you'd like to get insights from a combined 351 CDOs, CAOs, CIO, and CTOs who spread across North America, Europe and Asia-Pacific, and cover 14 sectors while running orgs generating at least $1B in annual revenue. We don't have to reinvent the wheel here. Let's learn from those who've tried it before, and adjust as needed to increase our chance of success. The key insights regarding the difficulties companies face when scaling ML use cases: No central place to store and discover ML models Numerous types of deployments and error prone hand-offs between data science and production Lack of ML expertise A plethora of tools and frameworks Hard to explain and govern ML models Outdated models because of infrequently refreshed data Access to relevant quality data Are you experiencing any of these? You can find more insights in the full doc below.