将AWS SageMaker ml模型转换为Java库

Question

When using AWS SageMaker, after you complete the training of a model, SageMaker will output the model as a model.tar.gz file in a specificed S3 bucket. The next step the documentation recommends is to deploy the model onto SageMaker. However, I do not want to deploy the model. In my case, there are some service to service latency considerations for not going that route. Furthermore, I would also like to still utilize predictions from the model in offline scenarios. Has anyone been able to take the model.tar.gz and make it into a java library? What tools did you use? How did you parse the model?

Answer 1

Most of the machine learning in the recent years are developed in Python and it is very common and performing well to serve the model with Python environments. You can see the flow in SageMaker documentation ( https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-hosting.html ), but this is completely open and can be achieved with NGNIX, GUnicorn.

You find some Java libraries for running some of the common machine learning algorithms, mainly: https://github.com/jpmml

Nevertheless, check if you must have Java as your run-time environment. Java will add very little value here (if any) and a lot of issues of compatibility.