📚 Jupyter Notebooks extension for versioning, managing and sharing notebook checkpoints in your machine learning and data science projects.
We have used the new hierarchical carbonate reservoir benchmarking case study created by Costa Gomes J, Geiger S, Arnold D to be used for reservoir characterization, uncertainty quantification and history matching.
Transform messy data science notebooks into production-ready code. Examples covering testing, CI/CD, MLOps, and scalable deployment practices.
Jupyter Notebook x Docker to Production on Heroku
Create plug-ins that expand and enhance the functionality of the i2 Notebook web client by using the i2 Notebook SDK. The SDK is comprised of documentation, tools, and sample code.
A boilerplate to transform research notebooks into production-level code which uses the a Kaggle dataset on customer churn.
Add a description, image, and links to the production topic page so that developers can more easily learn about it.
To associate your repository with the production topic, visit your repo's landing page and select "manage topics."