Operationalize-MicroService-API

Project Description

In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API. This project tests your ability to operationalize a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls.

Tasks to be Performed

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

Brief Description of Project Files

.circleci: Build pipeline for our microservice API

model_data : Folder that contains the pretrained sklearn model and housing csv files

output_txt_files: Folder that contains sample output logs from running ./run_docker.sh and ./run_kubernetes.sh

app.py : The flask application to be used

Dockerfile: Has instructions to containerize the application

Makefile : Has instructions for the environment setup and lint tests

requirements.txt: list of required dependencies

run_docker.sh: a bash script to build Docker image and run the application in a Docker container

upload_docker.sh: a bash script to upload the built Docker image to Dockerhub

run_kubernetes.sh: a bash script to run the application in a Kubernetes cluster

make_prediction.sh: a bash script to make predictions against the Docker container and k8s cluster

README.md: project's README file

Project_Screenshots: Checkout the screenshots that I took, while building this project

Instructions to get started

Create and activate a virtual environment.
Run make install and get the required dependencies.

Run the Flask App

1. Standalone: python3 app.py
2. Using docker:
      ./run_docker.sh
3. With Kubernetes:
      ./run_kubernetes.sh

Using Kubernetes

* Configure Docker locally.
* Configure Kubernetes locally.
* Containerize the flask application.
* Use kubectl to get the application running