COMActw / project-ml-microservice-kubernetes

Operationalize a Machine Learning Microservice API

Project Overview

This project focuses on applying the acquired skills in operationalizing a Machine Learning Microservice API. It involves the utilization of a pre-trained Scikit-learn model that predicts housing prices in Boston based on various features such as average rooms in a home, data about highway access, teacher-to-pupil ratios, and more. The project utilizes data that was initially obtained from Kaggle, and more information can be found on the data source site. The primary objective of the project is to operationalize a Python Flask app, which is provided in the app.py file. The app serves predictions (inference) about housing prices via API calls. The project is not limited to this specific machine learning model and can be extended to other pre-trained models such as those for image recognition and data labeling.

Getting Started

Install Docker

1. Update existing list of packages

     sudo apt update
   

2. Install a few prerequisite packages which let apt use packages over HTTPS

     sudo apt install apt-transport-https ca-certificates curl software-properties-common
   

3. Add the GPG key for the official Docker repository to your system

     curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
   

4. Add the Docker repository to APT sources

     sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu focal stable"
   

5. Make sure to install from the Docker repo

     apt-cache policy docker-ce
   

6. Install Docker

     sudo apt install docker-ce
   

7. Check that it’s running

     sudo systemctl status docker
   

Lints checks

Run lint make lint

Install K8s & Minikube

1. Install the latest minikube stable release on x86-64 Linux using binary download

     curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
     sudo install minikube-linux-amd64 /usr/local/bin/minikube
   

2. Start cluster

     minikube start
   

3. Download the latest K8s release with the command

     curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
   

4. Install kubectl

     sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl
   

5. Test to ensure the version is up-to-date

     kubectl version --client --output=yaml
   

Dockerfile

1. Config Dockerfile
2. Run Docker container
3. Make prediction
4. Logging the docker_out.txt file

Kubernetes

1. Config K8s
2. Deploy with K8s
3. Logging the kubernetes_out.txt file

CircleCI Integration

Verify the repo via CircleCI and add the status badge on top of this README