Modern research has benefited immensely from digitization efforts, e.g. through the big data wave, and digital tools have become ubiquitous in the scientific workflows of knowledge driven organizations. And while data science has made its impact, both data engineering and digital innovation in the business functions have taken a backseat position. But our ambition of advancing research through machine learning and AI requires more than just digitizing processes; it demands a shift towards business led digital innovation. This transition involves not only the use of digital tools but also fostering an environment where researchers can freely create and manage their digital assets. 

In recent years, Generative AI (GenAI) has progressed at an incredible pace. What once seemed out of reach for all but the biggest companies is now available to mid-sized businesses with just a few clicks. Large Language Models (LLMs) have become commoditized, meaning they are affordable, easy to access, and intuitive to use. With a simple setup—an LLM connected to an API, combined with a Retrieval-Augmented Generation (RAG) system and a user-friendly interface means that your company can get started on AI-driven solutions with minimal technical effort.

Jupyter ecosystem supports  data scientists, researchers, educators, and developers who work with data and computational notebooks. The platform is popular among users because it allows real-time interaction with data, enabling them to run code, visualize results, and make immediate adjustments. It’s a simple, yet powerful, software that provides a virtual lab environment, allowing individuals to experiment, analyze, and develop solutions independently. Setting up Jupyter organizations provides teams with shared, scalable access to data and computational resources, fostering collaboration and efficiency.