ML microservice with Nameko to implement a predictive maintenance application
In this article the development and deployment of a microservice is going to be explained. This microservice consists of:
The model has been developed based on random forest algorithm, the target is to predict the remained useful life based on current state and is export into joblib-file to be used in ml-service!
This part of project is a nameko service that get the prediction from model based on input data!
This service is built with fastapi and will ask ml-service to calculate the remained cycle of turbofan.
This service is will store data into hbase, the docker file has been developed here!
First this table at start will be created!
Then in nameko service the saving and querying data would be like this:
And finally in will represented like this in nameko:
A nameko predictive service for remainting useful life with fastapi as webservice Dismiss GitHub is home to over 50…