Plasticity

Plasticity

Plasticity is an open-source machine learning platform that allows users to develop, deploy and manage machine learning models without needing to write any code. It features a visual interface to build models, perform feature engineering, tune hyperparameters and monitor model pe
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opensource machine-learning ml-models visual-interface nocode

Plasticity: Open-Source Machine Learning Platform

A machine learning platform allowing users to develop, deploy and manage models without coding, featuring a visual interface for model building and performance monitoring.

What is Plasticity?

Plasticity is an open-source machine learning platform designed to make machine learning more accessible for non-technical users. With Plasticity's visual no-code interface, users can build, deploy and manage machine learning models without needing to write any code.

Some key features of Plasticity include:

  • Visual model builder - Build models by connecting predefined building blocks instead of coding models from scratch.
  • Automated machine learning - Plasticity can automatically test multiple algorithms and parameters to find the best model for your data.
  • Collaboration tools - Work together with team members on machine learning projects.
  • Model monitoring - Monitor your deployed models in production to check for data drift or performance degradation.
  • Model governance - Tools for model versioning, reproducibility, explainability and bias detection.

By abstracting away the underlying complexity, Plasticity allows domain experts with no coding experience to leverage the power of machine learning. The platform is free and open-source, with commercial options available for enterprise features and support.

Plasticity Features

Features

  1. Visual interface to build ML models without coding
  2. Perform feature engineering
  3. Tune hyperparameters
  4. Monitor model performance
  5. Deploy models

Pricing

  • Open Source

Pros

No-code platform

Open source and free

Intuitive visual interface

End-to-end ML workflow

Cons

Limited model and algorithm options

Less flexibility than coding ML from scratch

Steep learning curve for non-technical users


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