The code for this blog can be found in this GitHub repository. This will serve as a starting point, and it can be generalized to demo any custom ML model. As an example, we use a custom Amazon Rekognition demo, which will annotate and label an uploaded image. This post outlines how to build and host Streamlit apps in Studio in a secure and reproducible manner without any time-consuming front-end development. With the integrated development environment (IDE) of Amazon SageMaker Studio with Jupyter Lab 3, we can build, run, and serve Streamlit web apps from within that same environment for development purposes. Streamlit applications are useful for presenting progress on a project to your team, gaining and sharing insights to your managers, and even getting feedback from customers. As a data scientist, you may want to showcase your findings for a dataset, or deploy a trained model. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. With Streamlit, developing demo applications for your ML solution is easy.

Developing web interfaces to interact with a machine learning (ML) model is a tedious task.

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