Manifold vs Streamlit

Streamlit vs Manifold: What are the differences?

Developers describe Streamlit as "A Python app framework built specifically for Machine Learning and Data Science teams". It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API. On the other hand, Manifold is detailed as "A model-agnostic visual debugging tool for machine learning". Understanding ML model performance and behavior is a non-trivial process, given the intrisic opacity of ML algorithms. Performance summary statistics such as AUC, RMSE, and others are not instructive enough for identifying what went wrong with a model or how to improve it. As a visual analytics tool, Manifold allows ML practitioners to look beyond overall summary metrics to detect which subset of data a model is inaccurately predicting.

Streamlit and Manifold can be categorized as "Machine Learning" tools.

Some of the features offered by Streamlit are:

• Free and open source
• Build apps in a dozen lines of Python with a simple API
• No callbacks

On the other hand, Manifold provides the following key features:

• Performance Comparison View
• Feature Attribution View
• Histogram / heatmap

Streamlit and Manifold are both open source tools. Streamlit with 6.44K GitHub stars and 552 forks on GitHub appears to be more popular than Manifold with 778 GitHub stars and 58 GitHub forks.