The shift from batch to real-time machine learning is becoming increasingly common as companies seek to improve personalization and stay ahead of the competition. Real-time ML refers to generating predictions online with at least an hourly frequency, distinguishing it from batch ML which generates predictions on a periodic, infrequent basis. The key to making this transition lies in understanding when and whether real-time ML makes sense for your product or use case, considering factors such as accuracy, cold-start problem, costs, and user expectations. A thorough assessment of the industry, technology stack, and business needs is necessary to determine if real-time ML is a suitable fit. With proper evaluation and planning, companies can leverage real-time features and models to improve their products' responsiveness and competitiveness.