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Ten Mistakes to Avoid in Operationalizing Machine Learning
TDWI Member Exclusive

May 12, 2020

Machine learning—where systems examine data to identify patterns with minimal human intervention—is becoming part of the analytics fabric of many organizations as its competitive value becomes understood. Organizations are making use of machine learning (ML) technologies in numerous ways.

Some may sound familiar, such as using ML to build churn models or predict fraud. Others seem more revolutionary, such as using ML to diagnose cancer or improve crop yield. ML is being used across the enterprise and across industries, and those organizations that are already using ML technologies are gaining value from it.

Although many companies are excited about machine learning, they often overlook some key success factors, especially when it comes to deploying and operationalizing ML models into production. They typically think more about the front end of the process than what is needed to operationalize the models on the back end. Yet putting models into production is where the real value lies, so it is important not to make the interrelated ten mistakes described here.

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