Veles

Veles

Veles is an open-source machine learning platform focused on model inspection and debugging. It provides visualization tools to understand, diagnose, and improve machine learning models.
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Veles: Open-Source Machine Learning Platform

Veles is an open-source machine learning platform focused on model inspection and debugging. It provides visualization tools to understand, diagnose, and improve machine learning models.

What is Veles?

Veles is an open-source platform for machine learning development and operations. It enables data scientists and ML engineers to understand, diagnose, and improve their models more effectively.

Some key capabilities of Veles include:

  • Interactive visualization of machine learning models, allowing users to visually inspect model behavior
  • Tools for debugging models by detecting errors, bias, overfitting, and more
  • Monitoring capabilities to track model performance over time
  • Comparison of multiple models side-by-side
  • Model benchmarking to evaluate different modeling approaches
  • Integration with popular ML frameworks like PyTorch, TensorFlow, and scikit-learn

The goal of Veles is to make the development, testing, and maintenance of machine learning systems easier. With its focus on model inspection and troubleshooting, Veles aims to improve model quality, performance, and fairness through greater transparency and visibility into models.

Veles is written in C++ and Python and is free and open-source software under the Apache 2.0 license. It can be deployed on Linux, Windows, and macOS platforms.

Veles Features

Features

  1. Visualization tools for model inspection
  2. Tools for debugging machine learning models
  3. Model versioning and deployment management
  4. Model monitoring and alerting
  5. Integration with popular ML frameworks like PyTorch, TensorFlow, Keras
  6. Model explainability and interpretability tools

Pricing

  • Open Source

Pros

Open source and free to use

Helps improve model transparency and trust

Enables debugging of model failures

Visual interface makes models more interpretable

Can integrate with CI/CD pipelines for MLOps

Cons

Limited model support compared to commercial platforms

Less scalable for large models or datasets

Lacks some advanced features like automated monitoring

Steep learning curve for non-technical users

Official Links

Official Website
https://codisec.com/veles/

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