Project Overview

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

We 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. We can read more about the data, which was initially taken from Kaggle, on the data source site. This project tests 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

The 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 we will:

• Test the project code using linting
• Complete a Dockerfile to containerize this application
• Deploy the 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 code has been tested

---

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

• Run make lint to test the project code using linting, outputs example:

``` # See local hadolint install instructions: https://github.com/hadolint/hadolint # This is linter for Dockerfiles hadolint --ignore DL3013 --ignore DL3042 Dockerfile # This is a linter for Python source code linter: https://www.pylint.org/ # This should be run from inside a virtualenv pylint --disable=R,C,W1203 app.py

--------------------------------------------------------------------
Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00)

```

Running app.py

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

• make a prediction, run ./make_prediction.sh:

  ./make_prediction.sh
  

3. Setup and Configure Docker locally

   docker login
   

4. Upload image to dockerhub, run ./upload_docker.sh

Kubernetes Steps

• Setup and Configure Kubernetes locally, install from here, in this case i have a kubernetes cluster with kubeadm deployment.
• Deploy app in Kubernetes: ./run_kubernetes.sh
• make a prediction: ./make_prediction.sh, results are below

  NAME                                    READY   STATUS    RESTARTS   AGE
  ml-project                              1/1     Running   0          2m5s
  
  Forwarding from 127.0.0.1:8000 -> 80
  Forwarding from [::1]:8000 -> 80
  Handling connection for 8000
  
  [2021-04-11 07:17:38,021] INFO in app: JSON payload: 
  {'CHAS': {'0': 0}, 'RM': {'0': 6.575}, 'TAX': {'0': 296.0}, 'PTRATIO': {'0': 15.3}, 'B': {'0': 396.9}, 'LSTAT': {'0': 4.98}}
  [2021-04-11 07:17:38,042] INFO in app: Inference payload DataFrame: 
  CHAS     RM    TAX  PTRATIO      B  LSTAT
  0     0  6.575  296.0     15.3  396.9   4.98
  [2021-04-11 07:17:38,058] INFO in app: Scaling Payload: 
  CHAS     RM    TAX  PTRATIO      B  LSTAT
  0     0  6.575  296.0     15.3  396.9   4.98
  [2021-04-11 07:17:38,065] INFO in app: Output prediction: [20.35373177134412]
  127.0.0.1 - - [11/Apr/2021 07:17:38] "POST /predict HTTP/1.1" 200 -