Gradio vs Manifold

Manifold vs Gradio: What are the differences?

Manifold: 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; Gradio: *GUIs for Faster ML Prototyping and Sharing *. It allows you to quickly create customizable UI components around your TensorFlow or PyTorch models, or even arbitrary Python functions. Mix and match components to support any combination of inputs and outputs.

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

Some of the features offered by Manifold are:

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

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

• Customizable Components
• Multiple Inputs and Outputs
• Sharing Interfaces Publicly & Privacy

Manifold is an open source tool with 1.22K GitHub stars and 91 GitHub forks. Here's a link to Manifold's open source repository on GitHub.