Deployment of trained AI and machine learning models as reliable services that can be used by applications or users.
Deployment can include model packaging, inference services, APIs, resource management, logging, scaling, and monitoring.
The service can support web applications, internal systems, research platforms, and online AI services.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.