In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API.
You 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 tests your 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.
Your 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 you will:
- 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
You can find a detailed project rubric, here.
The final implementation of the project will showcase your abilities to operationalize production microservices.
- Create a virtualenv and activate it
- Run
make install
to install the necessary dependencies
- Standalone:
python app.py
- Run in Docker:
./run_docker.sh
- Run in Kubernetes:
./run_kubernetes.sh
- .circleci/config.yml - CircleCI configuration file
- Dockerfile - instructions how to containerize the app
- Makefile - instructions how to setup the environment, to lint, etc.
- README.md - this file ;)
- app.py - main application body
- output_txt_files/docker_out.txt - output of the docker container with LOG.info
- output_txt_files/kubernetes_out.txt - output of launching the app in minikube
- requirements.txt - list of required libraries for python
- run_docker.sh - script to build and run the docker container with the app
- run_kubernetes.sh - script to run the deployment (with the app containerized in the pod) and to do the port forwarding in minikube
- upload_docker.sh - script to upload the docker container to hub.docker.com
- make_predictions.sh - script to make actual predictions
- model_data/* - directory which contains the prebuilt model