Serverless ML Project

In this project, I apply the skills I acquired to operationalize a Machine Learning Microservice API.

There is 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 the 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.

My 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 I will:

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

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

• .circleci: For the CircleCI build server
• model_data : this folder contains the pretrained sklearn model and housing csv files
• output_txt_files: folder contains sample output logs from running ./run_docker.sh and ./run_kubernetes.sh
• app.py : contains the flask app
• Dockerfile: contains instructions to containerize the application
• Makefile : contains instructions for environment setup and lint tests
• requirements.txt: list of required dependencies
• run_docker.sh: bash script to build Docker image and run the application in a Docker container
• upload_docker.sh: bash script to upload the built Docker image to Dockerhub
• run_kubernetes.sh: bash script to run the application in a Kubernetes cluster
• make_prediction.sh: bash script to make predictions against the Docker container and k8s cluster
• README.md: this README file

• Create a virtualenv with Python 3.7 and 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> .devops
source .devops/bin/activate

• Run make install to install the necessary dependencies

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

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