Fully Managed D-Wave QBoost on AWS SageMaker
Hoping to help the community to grow and create production ready applications that solves real business use cases, I wanted to create a tutorial.
Here I try to explain how to train your model in the D-Wave machine, and serve it in a classical API endpoint, everything through AWS SageMaker.
The methodology described in the repository is general and it's meant to be modified, and extended in order to allow many different models and approaches for different problems.
The same procedure can be replicate in Google Cloud Platform using their managed services.
Furthermore I'm planning to create a more complete Medium article where I explain the above example from scratch, and extend it with serverless services like AWS Lambda & API Gateway to have Authentication and Load Balancing managed by the cloud servicer.
(Hint: ... Imaging cloud-agnostic architecture like Kubernetes cluster that use seamlessly CPU / GPU / QPU according the tasks, that automatically scale & optimise load balance. It's coming...slowly :) )
Thank you D-Wave & all the users! Keep rocking the Multiverse!!