Data scientists often struggle to share their work with stakeholders and get prompt feedback due to a lack of user-friendly platforms for real-time interaction with their models. Without these tools, the feedback process becomes slow and cumbersome, making it harder to iterate on ideas and improve their work.

At PagerDuty, we found that the absence of real-time interaction tools caused a ripple effect, slowing innovation and leading to delayed feedback loops, hindering model refinement. This also resulted in reduced collaboration among cross-functional teams, as stakeholders couldn't easily engage with data science projects. Ultimately, this leads to missed opportunities for innovation, especially in rapidly changing areas like machine learning and Generative AI. Without real-time tools, data scientists risk falling behind, compromising the organization's ability to remain competitive and adapt to technological changes.

A DS development platform like Streamlit can help solve these issues by enabling real-time interaction with AI Apps. It allows data scientists to share their work more easily, leading to faster feedback and improved collaboration, giving stakeholders confidence in the product before it's productionized.

This presentation will delve into the benefits that a DS development tool provides such as speed, collaboration, testing etc. which all contribute to quickly moving a project from ideation to production. Additionally, we will also walk through an example of a RAG GenAI application that started as a prototype but eventually evolved into a production feature.