Machine learning has been used in a variety of applications, including forecasting customer behaviour and developing the operating system for self-driving automobiles.
Machine learning can help organisations better understand their customers, which has a number of advantages. Machine learning algorithms can discover relationships and help teams customise product development and marketing campaigns to customer demand by gathering customer data and associating it with actions over time.
Several companies’ business models are heavily reliant on machine learning. Uber, for example, uses algorithms to connect drivers and riders. Google uses machine learning to surface transportation ads in searches.
Machine learning, on the other hand, has a number of disadvantages. To begin with, it may be pretty expensive. Machine learning programmes are frequently led by data scientists, who are well compensated. In addition, these initiatives demand an expensive software infrastructure.
In machine learning, there’s also the issue of bias. Algorithms trained on data sets that omit specific populations or have defects can produce faulty world models that fail at best and discriminate at worst. When a corporation’s critical business processes are based on skewed models, it exposes the company to regulatory and reputational risk.