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How to Operationalize Your Machine Learning Projects

How to Operationalize Your Machine Learning Projects

Operationalizing those data science, analytics, and machine learning projects is one of the top concerns of IT leaders. But the same tried-and-true best practices you’ve used for other IT projects can guide you on these new technologies, too.

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.

Operationalizing those data science, analytics, and machine learning projects is one of the top concerns of IT leaders. But the same tried-and-true best practices you’ve used for other IT projects can guide you on these new technologies, too. Everyone knows that to compete in the future, you need to […]

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