Real-time machine learning is a new operational approach that utilizes both batch and real-time data sources to make autonomous and continuous decisions in real time. It differs from analytical machine learning, which relies on human-in-the-loop decision-making and operates at human timescales. Real-time ML applications are mission-critical and run "online" in production on a company's operational stack, impacting business operations directly. The technical challenges of converting raw data into features and predictions remain the same across all real-time ML use cases. Modern trends enabling real-time machine learning include centralized data storage, long-term preservation of historical data, and the availability of real-time data through streaming infrastructure. The adoption of MLOps (Machine Learning Operations) principles is also crucial for scaling real-time ML models to meet business needs. To get started with real-time machine learning, one should choose a use case ideal for machine learning, select a high-potential use case, keep the team small and focused, and don't struggle alone by joining the MLOps community and learning from others' experiences.