SUMMARY
In this project, I applied the skills you have acquired in the udacity cloud devops nanodegree to operationalize a Machine Learning Microservice API.
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, was given.
my project goal was to operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. i went ahead to:
- Create a virtualenv with Python 3.7 and activate it
- Complete a Dockerfile to containerize this application
- Deploy the 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.
COMMAND LINE COMMANDS TO RUN APP
- To create a virtual environment: python3 -m virtualenv --python=<path-to-Python3.7> .devops
- To activate virtual environment: source .devops/bin/activate
- To install the necessary dependencies: make install
- To Run app in Docker: ./run_docker.sh
- To Run app in Kubernetes: ./run_kubernetes.sh
FILES
- Makefile: used for Installing dependencies
- requirements.txt: project dependencies are listed in the file
- app.py: The flask application file
- Model_data: Housing data used in building the model
- Dockerfile: contains the configuration for building the docker container
- upload_docker.sh: Shell script to upload the containerized image of application to dockerhub