Anovos
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About
Anovos is an open-source feature engineering library designed to assist data scientists in addressing data quality issues and enhancing model performance through effective feature creation. It allows users to understand their data better, resolve quality problems, and create optimal features for high-performing machine learning models. Anovos provides tools for data exploration, anomaly detection, custom feature creation, and comprehensive analytics. It is available in Community and Enterprise editions, supporting various data set sizes. With features like a feature recommender and data stability calculation, Anovos facilitates the building of resilient machine learning models. The tool is built upon popular libraries such as Pandas, Scikit-learn, and Apache Spark, ensuring scalability and efficiency for both individual users and enterprise teams.
Platform
Features
• open-source
• comprehensive analytics
• anomaly detection
• data exploration
• custom feature creation
• scalable data stability calculation
• feature recommender
• feature engineering tools
• data quality resolution
FAQs
What libraries are included in Anovos?
Anovos is built with the most popular machine learning libraries including Pandas, Scikit-learn, and Apache Spark.
Does Anovos scale?
Anovos can handle large data sets and is built to easily manage 100GB of data.
Is there a public roadmap for Anovos?
Yes, a current roadmap can be found in the Docs section. Suggestions can be made via GitHub.
Is Anovos ready for my team to use?
Yes, Anovos provides out-of-the-box analytics and metrics to help understand data and transform it into features for resilient models.
Pricing Plans
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