In short, any business with a data science team that produces ML models that address business operations.
The phrase “data-driven” is by now a cliche in the business world. Business decision-makers keep an eye on the bottom line with the help of internal data infrastructures. Depending on the industry, the method by which this value is extracted can look very different. In general, any business that collects and stores data can also train a model. Depending on the use case, models can deliver predictions and insights, and detect risks with data generated by the business.
MLOps becomes relevant when the model creation process is iterated, automated, and monitored so that the model continues to produce relevant results and business value as data continues to flow.