Source: Digital Insurance December 4, 2018
Author: Sivan Metzger
Machine learning adoption has been growing at a rapid pace, and there is no end in sight. A forecast by International Data Corporation shows that spending on artificial intelligence and ML will grow from twelve billion dollars in 2017 to about fifty-eight billion dollars in 2021.
In addition, Deloitte Global shows that the number of ML pilots and implementations will double in 2018 compared to 2017, and double again by 2020.
Yet, as you’ll see in my predictions for machine learning in 2019, the path to deriving real business ROI from AI and ML initiatives is still far from being an achievable feat for most companies.
Frustration among business leaders will continue to grow.
For many companies, ownership of machine learning initiatives lies with data science teams. Despite being well versed in choosing, building and validating training algorithms and turning them into models to solve a business problem, data scientists are not familiar with what it takes to deploy and manage those models in production – an aspect that is typically owned by the operations teams.