I recently had the privilege of presenting at JupyterCon 2025, where I shared our work on the Jupyter MCP Server that aims to standardize how AI agents interact with Jupyter notebooks through the Model Context Protocol (MCP).
This blog post details our approach to the AI for Earthquake Response Challenge, organized by the ESA Φ-lab and the International Charter "Space and Major Disasters". We secured 2nd place among over 250 teams by developing AI models capable of accurately detecting building damage from satellite imagery.
We are excited to announce that Datalayer has officially been awarded ISO/IEC 42001 certification, a significant milestone in our commitment to responsible AI governance and trustworthy AI deployment.
In the fast-evolving world of data science and AI, having the right tools and resources is critical for success. As datasets grow larger and computations more complex, data scientists need scalable, flexible, and reliable solutions to perform high-performance analyses. Datalayer allows you to scale your data science workflows with ease, thanks to its Remote Kernels solution. This feature enables you to run computations in powerful cloud environments directly from your JupyterLab, VS Code or CLI.
We have created a public GitHub repository with a collection of Jupyter Notebooks that showcases scenarios where Datalayer proves highly beneficial. These examples cover a wide range of topics, including machine learning, computer vision, natural language processing, and generative AI.
