singha53-zz / udacity-ML_microservice_kubernetes

Operationalize a Machine Learning Microservice API.

Using a pre-trained, sklearn model 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 operationalizes a Python flask app in, 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.

Setup the Environment

• Create a virtualenv and activate it:

git clone https://github.com/singha53/udacity-ML_microservice_kubernetes.git
cd udacity-ML_microservice_kubernetes
python3 -m venv venv
source venv/bin/activate

• Run make install to install the necessary dependencies
• install docker
• install hadolint using brew install hadolint (mac) to lint Dockerfiles. (test using make lint)
• Install VirtualBox using brew cask install virtualbox and install Minikube (mac) using brew install minikube

Running app.py

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

• Make predictions using ./make_prediction.sh

Kubernetes Steps

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