Project Overview

**In this project, I have applied the skills required to operationalize a production Machine Learning(ml) Microservice API. **

**By Gabriel Onike **

Here is 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 showcases my 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 Files

The projects 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, the files are described as follows:

• app.py - Machine Learning main file app
• model_data - simulated data for the main app
• Makefile - Install, Lint and Test your project code using linting
• Dockerfile - a Dockerfile to containerize this application
• run_docker.sh - Deploy your containerized application using Docker and make a prediction
• docker_out | kubernetes_out - some log statements in the source code for this application
• run_kubernetes.sh - Configure Kubernetes and create a Kubernetes cluster + Deploy a container using Kubernetes and make a prediction
• .circleci - Upload a complete Github repo with CircleCI to build and test code
• requirements.txt - python imports/required libraries for the ml service

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The Environment

• Create a virtualenv with Python 3.7 - remember to activate it. Refer to this link for help on specifying the Python version in the virtualenv.

python3 -m pip install --user virtualenv
# You should have Python 3.7 available in your host. 
# Check the Python path using `which python3`
# Use a command similar to this one:
python3 -m virtualenv --python=<path-to-Python3.7> .ml-project
source .ml-project/bin/activate

Build Flow

• Run a docker container

• Upload container into a public registry (hub.docker.com)

• Run the deployed application in a Kubernetes cluster

• Integrate with CircleCI for continuous integration

• 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

Other Important Commands - from Shell/Bash Terminal

• Make all activating makefile
• ./run_docker.sh OR sh run_docker.sh | ./run_kubernetes.sh | upload_docker.sh runs docker, runs kubernetes and uploading docker commands

REFERENCES

• https://stackoverflow.com/questions/61204189/vs-code-pylintimport-error-unable-to-import-subsub-module-from-custom-direct // solving pylint import errors