Why ModelOps is an enterprise-level capability under the CIO's accountability

Today, artificial intelligence applications are widespread and perceptible even in everyday life. Consider, for example, the ability of a car to brake automatically or not to exit the lane during a curve or, also, to the health sector, where the AI is able to monitor and report anomalous trends as well as helping to streamline processes to free up doctors' schedules and therefore reduce costs. Let's think for example of a computer able to identify the famous person we have in mind, asking ourselves no more than 10 questions, or how AI is constantly being adopted by many organizations in the finance and business sectors to restructure companies, improve earnings and experiences, reduce risks, and increase opportunities for the financial engines of our modern economy. These are already existing technologies of more or less advanced artificial intelligence, which are changing the way we live, work and study. When the machine learns independently, thanks to these and through experience, managing to improve its performance over time by providing more answers or more functions, you will have machine learning.