Machine learning (ML) is transforming the way organizations approach problem solving and product development. It allows machines to perceive, learn from, abstract, and act on data. Yet until recently, this discipline was accessible only to specialized data scientists in a process known as analytical ML—in which specialists created custom machine learning models to aid human-driven decision making.
The emergence of MLOps has provided a shift from analytical ML to operational ML. MLOps is a set of practices that allows for standardized collaboration between data scientists and operations, so organizations can manage the lifecycle of ML engineering and deployment. It has changed the game by allowing organizations to scale and standardize the power of AI, deliver trusted decisions in real time, and more efficiently develop products and solve problems.