The just concluded JupyterCon 2023 conference, held at Europe’s largest science museum, the , showcased infinite possibilities throughout the data science realm, empowered by the platform.

The conference, from May 10 to 12, provided worthwhile practical insight into how diverse teams, like data scientists, business analysts, educators, and researchers, can leverage the facility of the Notebook and other Jupyter tools to effectively, and the capabilities of their workflows, their projects, and make sure the of their data science endeavours.

We got to attend keynotes, tutorials, code sprints, and talks (where we marked the launch of our community-driven, open-source platform ) across three days.

A summary of some keynotes and insights is below;

• , professor of Astronomy at Harvard University, shared her insights on how Jupyter, along with the project, opens up latest opportunities for exploratory data exploration and visualisation in astronomy and beyond.
• , Nobel laureate economist and former Chief Economist of the World Bank, emphasised the transformative power of Jupyter. He discussed how Jupyter Notebooks revolutionise the research process and the communication of research findings. Romer highlighted the unique advantage of Jupyter’s interactive nature, enabling researchers to seamlessly integrate code, data, visualisations, and explanatory text right into a single dynamic document.
• Finally, and from GitHub presented the event and capabilities of GitHub Codespaces. They specifically addressed how Jupyter integration enhances this robust tool for collaborative code development and collaboration. GitHub Codespaces provides a strong platform for developers to work together effectively, and the combination of Jupyter Notebooks adds one other layer of functionality and adaptability to the tool.

Read our of highlights from the assorted JupyterCon tracks we found interesting this 12 months;

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A key takeaway from this 12 months’s conference was the deal with how the open-source communities’ landscape has undergone radical changes over the past twenty years. , founding father of and a co-founder of , underscored the importance of fostering a multi-stakeholder communitywithin the Jupyter context. He describes in his talk how, right from its inception, IPython and Jupyter were collaborative endeavours that thrived on quite a few individuals’ contributions, ideas, code, feedback, and dedication.

While he acknowledges that a way of vision and direction is important for the long-term success of any project, Perez argues that a ‘dictator’ is entirely the unsuitable metaphor to base that work on. He calls attention to how Jupyter has transitioned away from that model, with the aspiration for more projects to find higher ways to harness the collective energy of the community.

The multi-stakeholder approach embraces community tooling, including frontends, kernels, extensions, and other tools within the Jupyter ecosystem. Several talks saw expert speakers navigating the Jupyter landscape in diverse ways.

, Technical Director of and co-author of gave a chat discussing the recent advancements within the xeus stack and its flexible architecture that permits the event of kernels to run entirely within the browser. He explains how xeus simplifies the creation of recent kernels, particularly for languages a C or C++ API, allowing kernel authors to deal with language-specific points without the necessity to handle the protocol intricacies.

This 12 months’s conference also saw a growing recognition of the interactivity in Jupyter Notebooks, with quite a few open-source projects and widgets, like (Jupyter-based interactive computing that turns static HTML pages into interactive ones), (Jupyter widget that visualises live data pipelines in JupyterLab), and (Jupyter widget library that explores data through sound and fills the gap by providing many audio components). These extensions can integrate to supply a performant, intuitive interface allowing users to , and data dynamically.

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The conference covered various points of deploying Jupyter and JupyterHub at scale in industry, government, high-performance computing, science, education, and other settings. In consequence, Enterprises that integrate Jupyter into their data science workflow need to handle challenges related to security, scalability, collaboration, and optimising infrastructure for high-performance use cases.

of enterprise Jupyter Infrastructure this Summit:

1. Some organisations prefer to establish their Jupyter infrastructure on their very own servers or data centers. , an open-source Infrastructure Engineer at , shared insights about transforming 2i2c’s tooling for managing JupyterHubs and Kubernetes clusters. The goal is to enable multiple JupyterHub deployments and Kubernetes clusters through a single infrastructure repository. Preserving each JupyterHub community’s autonomy is an important focus, allowing them to extract their configuration from 2i2c’s system and independently deploy it elsewhere. This idea, the community’s Right to Replicate its infrastructure, holds significant importance.
2. Docker has gained popularity for managing Jupyter environments. Enterprises can create Docker containers with Jupyter and the vital libraries, dependencies, and extensions. , for instance, is an open-source platform propulsing Notebooks as Web applications composed of a Docker stack with multiple containers. These containers could be deployed and scaled using Kubernetes, making managing and distributing Jupyter environments easier across a cluster of machines.
3. Some enterprises use data science platforms, reminiscent of , which offer a collaborative environment for data scientists and analysts. , staff software engineer at Databricks, talked on the platform recently adopting Jupyter standards and software to power several features. He discussed how they encode Databricks-specific visualisations in exported Jupyter Notebook files in a way compatible with other Jupyter tooling. Also, he identified that in Databricks, the document state lives on the server, which changes how Jupyter kernel messages are processed.

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, the Director of Education at , dedicated his talk over with 10 Years of Teaching with Jupyter: Reflections from Industry and Academia. He spoke about his experience wrestling quite a few nuts-and-bolts technological obstacles to promoting computational pondering in learners with Jupyter.

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• robust, “99% invisible” computational environments so learners can start quickly (i.e., without painful software installation or configuration);
• data easily (especially when balancing privacy/security concerns with teaching goals); and
• Notebook versions for collaborating instructors.

He discusses several technological and non-technological approaches to tackle such challenges. These include , an open-source platform for distributing JupyterHub; , an extension that permits creating interactive slideshows in Jupyter, to support reproducibility and sharing of knowledge science projects; and for real-time collaborative editing. Jupyter Notebooks intentionally design to supply immediate learner feedback, allowing learners to switch code interactively and enhance their understanding autonomously — which is Aruliah’s ultimate goal when teaching.

The talks for Jupyter in Education paves the solution to leverage Education experiences and pedagogical support in reusable Jupyter Notebooks and Books.

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Our team at Oblivious had the chance to present , our eyes-off data science platform, to the Jupyter community. By leveraging privacy-enhancing technologies, we try to cultivate trust and establish a data-availability advantage.

We deal with privacy-centric use cases, enabling data scientists and builders to attach and extract insights from sensitive data while ensuring privacy through secure enclaves and differential privacy techniques. When users hook up with Antigranular, we offer a dedicated space and memory for program execution, securely manage their data on servers, and track code execution and session information using kernels for seamless collaboration and transparency. This arrangement ensures that the sensitive data stays protected while being accessible for evaluation.

get entangled? 🌎

Join our global community of privacy-minded data scientists and engineers from over 33 countries as we work together to shape the long run of privacy in machine learning.

Our platform is the perfect space to share your projects and concepts throughout the community; it opens the door to worthwhile feedback and support from fellow members who share your passion for privacy-focused data science! Start by joining our .

And don’t miss our upcoming in Dublin this July! Whether you like to attend in person or virtually, you possibly can register now to secure your spot. Join us for engaging panel discussions, and fireside talks featuring our expert speaker lineup, and get hands-on experience with privacy-enhancing technologies (PETs) through our tutorials, workshops, and live Hackathon.