Reproducible and shareable notebooks across a data science team
06-13, 14:50–15:30 (Europe/Berlin), Kesselhaus

At CybelAngel we scan the internet looking for sensitive data leaks belonging to our clients.
As the volume of alerts could count billions of samples, we use machine learning to throw away as much noise as possible to reduce the analysts' workload.

We are a growing team of data scientists and a machine learning engineer, planning to double in size. Each of us contributes to projects and we use Notebooks before code industrialisation. As for many other data science teams, a lot of effort and valuable work is encapsulated in a format that is tricky to share, hardly reproducible and simply not built for production purposes. During the talk, we will present what we did to overcome some of these issues and our feedback about notebook versioning and implementation in Google Cloud Platform using open JupyterHub and Jupytext.

This talk is addressed to a technical audience but all roles gravitating around a data team are welcome to grasp the challenges of the interaction of data science within the organisation.

I am a machine learning engineer at CybelAngel with a PhD in Computer Science. I like to work in a startup environment, also leading the development of machine learning products from idea to production. I am interested in cutting-edge technology, sharing knowledge and industrialization of Machine Learning.

Pascal is a Data Scientist at CybelAngel, Paris. He is focusing on building robust and efficient machine learning models to identify all kinds of digital threats. He also has a strong interest in various subjects related to Machine Learning Operations (MLOps). He is eager to solve the technological challenges of tomorrow in the AI field where innovation and knowledge sharing are paramount.