/supreme-funicular

DevOps P4. Operationalization of a Machine Learning Microservice API in Docker and Kubernetes.

Primary LanguagePython

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ajejoseph22

Project Overview

In this project, I operationalized a Machine Learning microservice API, containerized it and deployed to a Kubernetes cluster. I also set up continuous integration on Circle CI.

This project contains a sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, which was initially taken from Kaggle, on the data source site. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.

Installation steps

  • Clone the repository

To run in Docker, you need to have Docker installed and running on the host and then:

  • Run bash run_docker.sh. This would build the image using the Dockerfile and run the container on your host, forwarding traffic from port 80 in the container to post 8080 on your host.

To run in a Kubernetes pod, you need to have minikube installed and started on the host. minikube requires a virtualization tool such as virtual box, so you need to install that too. You can start minikube using minikube start and make use of the default cluster for deployments.:

  • Run bash run_kubernetes.sh. This will run the container in a Kubernetes pod and forward traffic from port 80 in the container to post 8080 on your host

That's it

Calling the prediction API

  • Run bash make_prediction.sh

Running without containerization

  • Install python 3.7
  • Run make setup to set up a venv and activate it
  • Install dependencies with make install
  • To lint the code run make lint
  • Run the server with python3 app.py
  • Change $PORT in make_prediction.sh to 80 and run bash make_prediction.sh to call the API