Industrial Internet Application Development

Containerization of machine learning models

In the real world, machine learning models can be developed in different languages, libraries, and technologies based on the use case to accelerate their development. In such a complex environment, containerization of the models provides a greater flexibility in the process of model operationalization. It provides a freedom for the developers to choose the right technology for the use case and also enables interaction with the legacy analytics under the same data infrastructure. It also provides the ability to execute the models at ultra scale with high availability and resiliency