Uber's reliance on operational machine learning has been key to its success, with the company leveraging this approach to power real-time decision making that directly impacts the end-user experience. Operational machine learning is distinct from analytical machine learning, focusing on autonomous and continuous decisions in production environments. The trend of modernizing data architectures and adopting DevOps principles has made it possible for companies to adopt operational ML, enabling rapid iteration and deployment. To get started with operational machine learning, businesses should identify suitable use cases, empower small teams, prioritize high-impact applications, and learn from others' experiences. With the right approach, operational ML can be a key differentiator between winners and losers in various industries.