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

You can find a detailed project rubric, here.

The final implementation of the project will showcase your abilities to operationalize production microservices.

---

Running app.py

1. Clone the repo

 $ git clone https://github.com/vonmutinda/MLOps.git

2. Install Deps

$ make setup
$ make install

NB: you may have to run the individual commands for setting up .venv

$ python3.7 -m venv .venv
$ source ./.venv/bin/activate
$ make install

3. Run appy.py

Serve the application on port 5050 instead of port 80 by modifying port on app.py L78

$ python app.py

output:

[2022-08-14 18:29:48,806] 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}}
[2022-08-14 18:29:49,235] INFO in app: Inference payload DataFrame: 
   CHAS     RM    TAX  PTRATIO      B  LSTAT
0     0  6.575  296.0     15.3  396.9   4.98
[2022-08-14 18:29:49,240] INFO in app: Scaling Payload: 
   CHAS     RM    TAX  PTRATIO      B  LSTAT
0     0  6.575  296.0     15.3  396.9   4.98
[2022-08-14 18:29:49,302] INFO in app: 

Ouput Prediction: [20.35373177134412]

127.0.0.1 - - [14/Aug/2022 18:29:49] "POST /predict HTTP/1.1" 200 -

4. Make a predicition

$ ./make_prediction.sh

output:

(.venv) ➜  MLOps git:(main) ✗ ./make_prediction.sh 
 Port: 5050
 {
  "prediction": [
    20.35373177134412
  ]
 }

Running App Docker

• Ensure Docker Desktop is running

  $ docker version

output:

$ MLOps git:(main) ✗ docker version
Client:
 Cloud integration: v1.0.28
 Version:           20.10.17
 API version:       1.41
 Go version:        go1.17.11
 Git commit:        100c701
 Built:             Mon Jun  6 23:04:45 2022
 OS/Arch:           darwin/arm64
 Context:           default
 Experimental:      true

Server: Docker Desktop 4.11.1 (84025)
 Engine:
  Version:          20.10.17
  API version:      1.41 (minimum version 1.12)
  Go version:       go1.17.11
  Git commit:       a89b842
  Built:            Mon Jun  6 23:01:01 2022
  OS/Arch:          linux/arm64
  Experimental:     false
 containerd:
  Version:          1.6.6
  GitCommit:        10c12954828e7c7c9b6e0ea9b0c02b01407d3ae1
 runc:
  Version:          1.1.2
  GitCommit:        v1.1.2-0-ga916309
 docker-init:
  Version:          0.19.0
  GitCommit:        de40ad0

• Run in Docker

  $ ./run_docker.sh

Running App in Kubernetes

• Ensure Kubernetes is running

  $ kubectl version --output=json

output:

{
  "clientVersion": {
    "major": "1",
    "minor": "24",
    "gitVersion": "v1.24.2",
    "gitCommit": "f66044f4361b9f1f96f0053dd46cb7dce5e990a8",
    "gitTreeState": "clean",
    "buildDate": "2022-06-15T14:22:29Z",
    "goVersion": "go1.18.3",
    "compiler": "gc",
    "platform": "darwin/arm64"
  },
  "kustomizeVersion": "v4.5.4",
  "serverVersion": {
    "major": "1",
    "minor": "24",
    "gitVersion": "v1.24.0",
    "gitCommit": "4ce5a8954017644c5420bae81d72b09b735c21f0",
    "gitTreeState": "clean",
    "buildDate": "2022-05-03T13:38:19Z",
    "goVersion": "go1.18.1",
    "compiler": "gc",
    "platform": "linux/arm64"
  }
}

• Run via kubectl

  $ ./run_kubernetes.sh

Useful Guide

• Makefile is rigged with handful commands to get you started in no time.
• If it takes too long to build an image, try the following deps in your requirements.txt file instead to see if the image build without errors.

  Click==7.0
  Flask==1.0.2
  itsdangerous==1.1.0
  Jinja2==2.10.3
  MarkupSafe==1.1.1
  numpy==1.17.2
  pandas==0.24.2
  python-dateutil==2.8.0
  pytz==2019.3
  scikit-learn==0.20.3
  scipy==1.3.1
  six==1.12.0
  Werkzeug==0.16.0
  pylint==2.4.4
  black