Project 4 -- cloud devops nanodegree -- Operationalize a Machine Learning Microservice API

I was given a pre-trained, 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. operationalize a Python flask app—in a provided file, app.py—that 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.

Project Tasks

My project goal is to operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. In this project I did:

• Test your project code using linting
• Complete a Dockerfile to containerize this application
• Deploy your containerized application using Docker and make a prediction
• Improve the log statements in the source code for this application
• Configure Kubernetes and create a Kubernetes cluster
• Deploy a container using Kubernetes and make a prediction
• Upload a complete Github repo with CircleCI to indicate that your code has been tested

The files included are:

* /images : Screenshot the result of deploy.
* /.circleci : CircleCI configuration file for running the tests
* /model_data : Housing model data
* /output_txt_files : Log of Output 
* Dockerfile : Dockerfile for building the image 
* Makefile : includes instructions on environment setup and lint tests
* app.py : Python flask app that serves out predictions (inference) about housing prices through API calls
* make_prediction.sh : Send a request to the Python flask app to get a prediction, for localhost 
* requirements.txt : Install any dependencies 
* run_docker.sh : file to be able to get Docker running, locally
* run_kubernetes.sh : file to run the app in kubernetes
* upload_docker.sh : file to upload the image to docker

Creation and activation of the environment

1. Docker installation
2. Lints checks with hadolint and pylint
3. Installation of Kubernetes and Minikube

Dockerfile

1. Dockerfile configuration
2. Run a Container & Make a Prediction
3. Logging in the docker_out.txt file

Kubernetes

1. Configure Kubernetes to Run Locally
2. Deploy with Kubernetes
3. Savings Output logs in the file kubernetes.out.txt

CircleCI Integration

This repository has been verified with CircleCI

Setup the Environment

• Create a virtualenv and activate it
• Run make install to install the necessary dependencies

Running app.py

1. Standalone: python app.py
2. Run in Docker: ./run_docker.sh
3. Run in Kubernetes: ./run_kubernetes.sh

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally
• Create Flask app in Container
• Run via kubectl

Run the project:

* Please follow to steps of screenshot in images folder