Jupyter in Production at Rev 3
I was pleased to be invited to present the talk Jupyter in Production at the Rev 3 MLOps Conference in New York City. Jupyter Notebooks have become an essential part of the data scientist’s toolkit, but deploying work developed in notebooks into production settings can be painful. Over the last few years, I’ve been excited to see a new ecosystem of tools emerge that push the limits of what you can do with Jupyter Notebooks in production environments.
In this talk, I highlight how some of these tools can help you to develop and distribute software libraries, build and run data pipelines, and create and serve interactive reports and dashboards — all without leaving Jupyter Notebooks. You can find the slides for my talk below.
Linked References and Resources
- nbdev
- ploomber
- voila
- parente/nbestimate: Estimate of Public Jupyter Notebooks on GitHub
- Congratulations to the LIGO and VIRGO Collaborations from Project Jupyter
- Jupyter receives the ACM Software System Award
- nbdev: use Jupyter Notebooks for everything
- nbdev: A Minimal Example
- nbdev: Fix merge conflicts
- nbdev tutorial
- How nbdev helps us structure our data science workflow in Jupyter Notebooks - Overstory
- nbdime — diffing and merging of Jupyter Notebooks
- ReviewNB - Jupyter Notebook Code Reviews & Collaboration
- nbQA
- ploomber use cases — Machine Learning
- Part 2: Scheduling Notebooks at Netflix - Netflix Technology Blog
- ploomber — Basic concepts
- ploomber — Your first Python pipeline
- jupytext
- sysuin/covid-19-world-dashboard: Interactive visualizations of the impact of covid-19 around the world
- dhaitz/machine-learning-interactive-visualization: Interactive visualization of machine learning model evaluation metrics
- voila-dashboards/voila-vuetify: Dashboard template for Voilà based on VuetifyJS
This post was originally published on datatheoretic.com.
