Let’s say your company’s data science teams have documented business goals for areas where analytics and machine learning models can deliver business impacts. Now they are ready to start. They’ve tagged data sets, selected machine learning technologies, and established a process for developing machine learning models. They have access to scalable cloud infrastructure. Is that sufficient to give the team the green light to develop machine learning models and deploy the successful ones to production?
Not so fast, say some machine learning and artificial intelligence experts who know that every innovation and production deployment comes with risks that need reviews and remediation strategies. They advocate establishing risk management practices early in the development and data science process. “In the area of data science or any other similarly focused business activity, innovation and risk management are two sides of the same coin,” says John Wheeler, senior advisor of risk and technology for AuditBoard.
