amroakmal / Operationalize-a-Machine-Learning-Microservice-API

Project Overview

In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API.

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

Project Tasks

Your 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 you will:

• 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

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Setup the Environment

• Create a virtualenv and activate it python3 -m venv ~/.devops
source ~/.devops/bin/activate

• Run make install to install the necessary dependencies

• For Ubuntu users, you must install hadolintto the project directory and make sure that its added to the /usr/bin/ To test hadolint alone, run: hadolint Dockerfile
  If it did not give any output, this means its working fine. Dockerfile is the file that has the steps to set up your image and create a container later from it.

Running app.py

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

Upload Dockerfile to DockerHub

1. Create an account on DockerHub and have a unique ID
2. ./run_docker.sh
3. ./upload_docker.sh This operation may take a while to finish.

Kubernetes Steps

Our goal:

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

Minikub MUST be running locally and started before being able to use `kubectl` and execute its commands.
Steps: 1- Download and install minikube locally accroding to your operating system 2- To start minikube run `minikube start` 3- Install `kubectl` locally 4- Make sure it's installed correctly by running `minikube version` 4- Check it works by running a command `kubectl version` Now we're able to make predictions.
1- Run `./run_docker.sh`
2- Run `./make_prediction.sh`

Directory files description

1- output_txt_files: For storing the output produced by making predictions using docker and kubernetes. 2- Dockerfile: Has the necessary steps to setup your docker image and create a container 3- make_prediction.sh: A shell script that makes a prediction 4- Makefile: A file where we can split up our steps to several ones and specify a tage name for each step, and able to run steps in the CLI using make <step_name 5- requirements.txt: Has all the needed libraries and dependencies to be able to complete the project. 6- run_docker.sh: A template shell script for easness of running steps needed for building a docker image, listing all current containers and creating a container. 7- run_kubernetes.sh: A shell script for easniss of setting and running a docker container using kubernetes. 8- upload_docker.sh: A shell script for easeniss of uploading a local Docker image to your Dockerhub online using your unique Docker ID