The IBM Data Science Experience (DSX) enables collaboration of data scientists, AI professionals, business analysts, and others to collaborate on projects, using tools such as Jupyter, ML training, dashboards, data shaping, data pipelines, and more in one integrated environment on the cloud. Explore Jupyter in DSX in detail and dive into end-to-end user scenarios where users create a project, collaboratively connect data, connect AI services, and use Jupyter notebooks to access data and AI services using Python, applying AI to the data, and visualizing the results.
You’ll learn how Jupyter was integrated into DSX Projects to achieve a seamless user experience and how Jupyter + Python/R runtimes are run at scale in the cloud 24×7, using Docker containers running on Kubernetes across availability zones and in multiple regions. You’ll also see how Jupyter notebooks and data assets can be shared more broadly across the enterprise in a data catalog and how Jupyter notebooks can then be reused in other projects, accessing data from the catalog. Along the way, you’ll encounter interesting examples of Jupyter notebooks usage across IBM, including notebooks for performance analysis of accessing cloud services, quantum computing notebooks, and more.
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