Machine Learning with Docker and Kubernetes: Batch Inference

You can find all the files used in this chapter on GitHub. Compared to our previous Dockerfile, we just added inference.py Here, we will download our previously trained models (Linear discriminant analysis and a multi-layer perceptron neural network) stored in a specified directory (/home/xavi/output) from a remote server (192.168.1.11) Once the image is successfully uploaded to the registry, we go to our project directory (connect to kubmaster) and create a configuration file, inference.yaml, We are finally ready to get our application running on Kubernetes.