Recent developments in Jupyter—from JupyterLab to Binder to open source clients like nteract—have created opportunities to solve difficult data problems such as scalability; reproducible science; and compliance, data privacy, ethics, and security issues.
That’s why Project Jupyter, the NumFOCUS Foundation, and O’Reilly Media have come together to host JupyterCon. It’s a unique opportunity to see how Jupyter’s creators, developers, and innovators across the industry, government, and academia are using Jupyter to solve common problems—and how their solutions could be applied in your organization.
In just a few days, you’ll get a chance to learn how to build a flexible, future-proof, and highly scalable shared data infrastructure; create reproducible and iterative analysis, and leverage these powerful tools for sharing and communicating data analysis.
And in those two days, you can witness in-depth education on critical topics. Training courses take place August 21-22 and are limited in size to maintain a high level of learning and instructor interaction.
Serverless Machine Learning with TensorFlow
Reproducible research best practices (highlighting Kaggle Kernels)
Explore the AWS machine learning platform using Amazon SageMaker
Hands-on data science with Python
Machine learning at scale with Kubernetes
Below you can watch sessions and interviews from JupyterCon 2017.