Industrial Internet Application Development

Machine learning as a service

At an enterprise level, multiple systems often need to be integrated to exchange the data between different systems. In such scenarios, service-oriented architecture provides a better flexibility in the integration of the systems for various reasons, such as the type of language and platform. After the model is built, the ML model as a service endpoint provides a way to integrate with the external system and can interact with the model for predictions. It provides a greater flexibility and extensibility of ML models to interact with various other models instead of execution in a silo. Now, we can have an network of ML models working together, exchanging the predictions across the enterprises with multi-tenancy ...