Xcessiv vs Streamlit: What are the differences?

Developers describe Xcessiv as "Fully managed web application for automated machine learning". A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python. On the other hand, Streamlit is detailed 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.

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

Some of the features offered by Xcessiv are:

• Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code
• Any model following the Scikit-learn API can be used as a base learner
• Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches

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

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

Xcessiv and Streamlit are both open source tools. It seems that Streamlit with 2.73K GitHub stars and 184 forks on GitHub has more adoption than Xcessiv with 1.2K GitHub stars and 101 GitHub forks.