Igel vs ML Visualization IDE
Overview
Detailed Comparison
It is a delightful machine learning tool that allows to train, test and use models without writing code. | Debug your machine learning models in realtime with powerful, interactive visualizations. Quickly log charts from your Python script, visualize your model development in live dashboards, and share interactive plots with your team, in just 2 minutes. |
Supports all state of the art machine learning models (even preview models);
Supports different data preprocessing methods;
Provides flexibility and data control while writing configurations;
Supports cross validation;
Supports both hyperparameter search (version >= 0.2.8);
Supports yaml and json format;
Supports different sklearn metrics for regression, classification and clustering;
Supports multi-output/multi-target regression and classification;
Supports multi-processing for parallel model construction | Powerful, interactive visualizations; Quickly log charts; Visualize your model development in live dashboards;
Share interactive plots with your team |
Statistics | |
GitHub Stars 3.1K | GitHub Stars - |
GitHub Forks 201 | GitHub Forks - |
Stacks 0 | Stacks 2 |
Followers 6 | Followers 8 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Igel, ML Visualization IDE?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
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.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
