Transfer knowledge and models created in other languages

Data scientists will often explore different methods to tackle an ML problem in a different language, such as Python, and produce a model that can solve the problem outside any application. The plumbing, such as getting data in and out of the model, serving this to a customer, persisting outputs or inputs, logging errors, or monitoring latencies, is not part of this deliverable and is outside the normal scope of work for a data scientist. As a result, taking the model from concept to a Go production application requires a polyglot approach such as a microservice.

However, there are Go bindings for deep learning frameworks such as TensorFlow and Caffe. Moreover, for more basic algorithms such as decision trees, the same algorithms have also been implemented in Go libraries and will produce the same results if they are configured in the same way. Together, these considerations imply that it is possible to fully integrate data science products into a Go application without sacrificing accuracy, speed, or forcing a data scientist to work with tools they are uncomfortable with.