India, April 3 -- Everyone knows that to compete in the future, you need to invest in machine learning, artificial intelligence, data, and analytics. But there still can be a big gap between knowing that you need to do it and figuring out how to do it in a way that is meaningful for your business.
Putting these technologies into production systems continues to be a challenge for many enterprises, according to Erick Brethenoux, a research director at Gartner.
"Development is academic. Production is economics," Brethenoux said during the session Operationalizing Data Science and Machine Learning Initiatives, delivered at the Gartner Data and Analytics Summit in Orlando, Florida.
Yet many of the best practices for putting these technologies...