Building well designed and easy to use web applications requires a significant amount of knowledge and experience across a range of domains. This can act as an impediment to engineers who primarily work in so-called back-end technologies such as machine learning and systems administration. In this episode Adrien Treuille describes how the Streamlit framework empowers anyone who is comfortable writing Python scripts to create beautiful applications to share their work and make it accessible to their colleagues and customers. If you have ever struggled with hacking together a simple web application to make a useful script self-service then give this episode a listen and then go experiment with how Streamlit can level up your work.
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- Your host as usual is Tobias Macey and today I’m interviewing Adrien Treuille about Streamlit, an open source app framework built for machine learning and data science teams
- How did you get introduced to Python?
- Can you start by explaining what Streamlit is and its origin story?
- What are some of the types of applications that are commonly built by data teams and who are the typical consumers of those projects?
- What are some of the challenges or complications that are unique to this problem space?
- What are some of the complications or challenges that you have faced to integrate Streamlit with so many different machine learning frameworks?
- Can you describe the technical implementation of Streamlit and how it has evolved since you began working on it?
- How did you approach the design of the API and development workflow to tailor it for the needs and capabilities of machine learning engineers?
- If you were to start the project from scratch today what would you do differently?
- What is a typical workflow for someone working on a machine learning application and how does Streamlit fit in?
- What are some of the types of tools or processes that it replaces?
- What are some of the most interesting or unexpected ways that you have seen Streamlit used?
- What have you found to be some of the most challenging or unexpected aspects of building and evolving Streamlit?
- How do you see Python evolving in light of Streamlit and other work in the machine learning space?
- What do you have in store for the future of Streamlit or any adjacent products and services?
- How are you approaching the governance and sustainability of the Streamlit open source project?
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