Get Started

The Jupyter notebooks in this project are intended to be comprehensive, end-to-end notebooks, going through each phase of the machine learning workflow - data understanding and exploration, data cleaning, feature engineering, model training, and model interpretation. So going through these notebooks is a great way to get started with the project.

Launch Project and Run Notebooks via JupyterHub

To make the notebooks reproducible and executable by everyone, we have containerized and deployed them on the public JupyterHub instance on the Massachusetts Open Cloud (MOC). So you can get access to a Jupyter environment and run our notebooks in just a few clicks! To do so, please follow the steps below:

1. Visit our JupyterHub, click on Log in with moc-sso and sign in using your Google Account.
2. On the spawner page, select OpenShift Anomaly Detection Notebook Image for notebook image, Large - Memory Intensive for container size, and then click Start server to spawn your server.
3. Once your server has spawned, you should see a directory titled openshift-anomaly-detection-<current-timestamp>. All the notebooks should be available inside the notebooks directory in it for you to explore.

Note: When you’re done running the notebooks, please go to File -> Hub Control Panel and click Stop My Server to shut down your JupyterHub pod.

Blog Post and Conference Talk

In addition to exploring the notebooks, you can also read our blog post to get a brief overview of the diagnosis discovery project. Additionally, you can also check out our conference talk at DevConf.CZ 2021 for an in-depth presentation and discussion.