Jupyter has become a critical component of the machine learning life cycle. However scaled enterprise deployments and making the Data Science Experience frictionless remain challenging. We address a few common issues with PrimeHub, an open-source enterprise offering based on JupyterHub, and investigate MLOps trends adjacent to the Jupyter ecosystem.
This talk is intended for audience interested in larger scale Jupyter environment deployment in their organisation, particularly for machine learning applications.
PrimeHub is an open-source enterprise offering based on JupyterHub, addressing a few common hurdles:
We also investigate a few trends adjacent to the day-to-day jupyter environment used by data scientists and data engineers, where the roles become more cross functional in the age of MLOps:
working with job schedulers from within Notebook
managing ML model deployments