In this project, we operationalize a Machine Learning Microservice API.
We 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.
Our 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.
What we did was:
- 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 and 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
- Model data that we need to make the prediction
- output_txt_files: The output which we got when we made the calls to the api
- Dockerfile: the Dockerfile we used to containerize our application
- Makefile: Our Makefile to run all the steps needed in an automated highway
- app.py: the code of our app
- sh files: The scripts we created to automate commands
- requirements.txt: Our requirements txt with all the dependencies needed