About
Collaborative Data Science becomes increasingly important, as organizations continue to become more data-driven, and Data Science projects/models become more complex. In the report **Critical Capabilities for Data Science and Machine Learning Platforms** (March 2021) Gartner predicts, that in near future collective intelligence in Data Science and cloud-based AI infrastructure will be among key factors for competitive advantage. This talk presents Distributed Data Science environments (part of [ScienceMesh](https://sciencemesh.io/)), which allow collaboration on [Jupyter Notebooks](https://jupyter.org/) in sync-and-share environment. Jupyter Notebook has become No1 platform used by data scientists to build interactive applications and to work with big data and AI. It is widely used in CS3 institutions, many successful applications have been presented in CS3 conferences. ScienceMesh, developed in [CS3MESH4EOSC](https://cs3mesh4eosc.eu/) project, creates the Federated Scientific Mesh providing federated sharing of data across different sync-and-share services, federated use of applications (such as collaborative document editing, data archiving, and data publishing), fast transfer of large datasets and remote data analysis (Data Science environments). For Data Science environments ScienceMesh delivers a [JupyterLab](https://jupyterlab.readthedocs.io/) extension, integrating JupyterLab environment with ScienceMesh. File browsing and additional share and collaboration functionalities for notebooks and resources across federated cloud are now possible in JupyterLab environment. JupyterLab is considered a complete, full-fledged IDE for Data Science tasks and interactive computing, where data scientists can do all their work in one tool, so the point is that functionalities for sharing (full [cs3apis](https://github.com/cs3org/cs3apis) client) and concurrent editing are available inside this environment. On the other hand, Data Science environments are integrated with a…
The talk
Credits and licence
- Institution
- CERN
- Department
- HEP Computing
- Speaker
- Marcin Sieprawski
- Year
- 2022
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed