We believe this approach enables our DS and ML teams to experiment faster with notebooks by easily accessing code, data, and tools. Our ongoing goal is to provide a managed Jupyter Notebook environment, which is integrated and compatible with Twitter’s data and development ecosystem. In this blog post, we will discuss how we implemented the following features to improve the notebook experience:

Notebooks are now an integral part of our Data & ML Platform narrative. This group took Twitter Notebook from an ambitious, early vision all the way to a top-level company initiative with 25x+ internal usership growth. This effort began within a small working group of engineers across different teams and backgrounds with the high-level goal of introducing Notebooks as a de facto tool for leveling up Twitter’s Data Science and ML Development platform capabilities. Twitter Notebook is our internal notebook solution that started as a small grassroots effort within Cortex around 2016. Within Cortex Platform (Twitter’s ML Platform group), the Experimentation Tools (ET) team is chartered with providing a first-class platform and tooling for data scientists, ML researchers, and ML engineers at Twitter.

Jupyter notebooks