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.

In today’s data-driven landscape, citizen data scientists—experts in various domains who have acquired data science and programming skills—play a critical role. They have the potential to bridge the gap between those who generate data analytics (analytics creators) and those who depend on data insights (analytics consumers).