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EVENT DETAILS |
Leverage the power of Jupyter for collaborative, extensible, scalable, & reproducible data science.
Scalable, reproducible, & powerful
Discover the expanding capabilities of the Jupyter ecosytem
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; & compliance, data privacy, ethics, & security issues.
That's why Project Jupyter, the NumFOCUS Foundation, & O'Reilly Media have come together to host JupyterCon. It's a unique opportunity to see how Jupyter's creators, developers, & innovators across industry, government, & 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 learn how to build a flexible, future-proof, & highly scalable shared data infrastructure; create reproducible & iterative analysis, & leverage these powerful tools for sharing & communicating data analysis.
Join us at JupyterCon in New York August 21-24. Need help convincing your manager? We've got you covered.
What you'll find at JupyterCon
Enterprise: Find out how the world's most innovative organizations build shared data infrastructure at scale with Jupyter-and how they approach compliance, data privacy, ethics, & security.
Education: Learn about Jupyter's impact in education-and how major universities are retooling their hands-on teaching programs.
Science: See firsthand how Jupyter supports reproducible science, whether in scientific research, data journalism, or data science teams in industry.
JupyterCon Training Courses
Get two days of in-depth education on critical topics. Training courses take place August 21-22 & are limited in size to maintain a high level of learning & 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
JupyterLab training
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