In this project, I applied the skills I have acquired in this course to operationalize a Machine Learning Microservice API.
I was 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.
To practicalize all of the techniques and methods taught in the "Microservices at scale, using AWS and Kubernetes" module
The 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.
- Python
- Flask
- Kubernetes
- Docker
- Pylint
- Hadolint
- CircleCI
In this project I:
- Tested the project code using linting
- Completed a Dockerfile to containerize this application
- Deployed the containerized application using Docker and made a prediction
- Improved the log statements in the source code for this application
- Configured Kubernetes and created a Kubernetes cluster
- Deployed a container using Kubernetes and made a prediction
- Uploaded a complete Github repo with CircleCI to indicate that the code has been tested
- Create a virtualenv with Python 3.7 and activate it. Refer to this link for help on specifying the Python version in the virtualenv.
python3 -m pip install --user virtualenv
python3 -m virtualenv --python=<path-to-Python3.7> .devops
source .devops/bin/activate
- Run
make install
to install the necessary dependencies - Run
make lint
to check for linting errors
Run ./run_docker.sh
to start the docker container, and create the flask app within the container, then run ./make_prediction.sh
to make predictions
Install minikube and virtualbox, then run minikube start
to start a local cluster
Run ./run_kubernetes.sh
to start the kubernetes pod and create the flask app in the container, then run ./make_prediction.sh
to make predictions, run_kubernetes.sh contains kubectl commands for running the container with kubernetes