Machine Learning Microservice API with Kubernetes

This project contains a machine learning model that has been trained to predict housing prices in Boston according to several features and it shows how to operationalize a Python flask app—in serves out predictions (inference) about housing prices through API calls.

This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.

Installation

1. Setup the environment pre requisites:

• Python
• Docker
• Docker Hub account
• Kubernetes
• minikube

2. On your bash console run

   make

   make install

Usage

1. Standalone:

   python app.py

2. Run in Docker:

   ./run_docker.sh

3. Run in Kubernetes:

   minikube start

   ./run_kubernetes.sh

Project files

• Makefile A Makefile which defines the set of tasks to be executed for this project installation.

• Dockerfile Docker container definition for running the house price predictor service.

• requirements.txt Python libraries dependencies.

• Bash Scripts

  • create_localenv.sh: setup a python virtual enviroment and install hadolint

  • make_prediction.sh: runs a post request to teest the predictor service.

  • run_docker.sh: docker commands to build and run the predictor stand alone container

  • run_kuberenets.sh: kubectl commands to set-up and run the kubernetes predictor service

  • upload_docker.sh: upload the container image to Docker Hub account.

Resources

Dataset

• You can read more about the data, which was initially taken from Kaggle, on the data source site.

License