In this project, I applied the skills acquired in the Udacity DevOps Nanodegree course to operationalize a Machine Learning Microservice API. This project was created using an AWS Cloud9 environment with an EC2 instance running Ubuntu Linux 18.04.
The given pre-trained sklearn model to use 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.
The premise of the project was to operationalize a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls.
The project goal is to operationalize this working, machine learning microservice using kubernetes.
- 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
- Create a virtualenv by running
make setupand activate it - Run
make installto install the necessary dependencies
- Standalone:
python app.py - Run in Docker:
./run_docker.sh - Run in Kubernetes:
./run_kubernetes.sh
- Setup and Configure Docker locally
- Setup and Configure Kubernetes locally
- Create Flask app in Container
- Run via kubectl
- Docker
- Kubernetes
- Minikube
- Python
- Flask
- CircleCI