NannyML provides Python tools for post-deployment data science and monitoring model performance under data drift.
Open source
24 alternativesigel
igel lets users train, test, and use machine learning models without writing code.
About igel
igel is an open-source tool that trains, tests and applies machine learning models without writing code. Users describe the task in a yaml or json configuration file.
It supports regression, classification and clustering, reads csv, txt, excel, json, html and folder-based raw data, and includes auto-ml for image and text classification. It also supports data preprocessing, cross validation, hyperparameter search, multi-output targets, and model serving for use from the terminal, Python or Docker.
Key features
- Train, test and run models using yaml or json config
- Supports regression, classification and clustering tasks
- Reads csv, txt, excel, json, html and folder-based raw data
- Provides auto-ml for image classification and text classification
- Supports cross validation and hyperparameter search
- Serve trained models and use via API
Best for: Technical and non-technical users who want to build machine learning prototypes without writing code.
Good for
- Works from yaml or json files without writing code
- Supports regression, classification and clustering with common dataset formats
- Open-source under MIT license with Python, Docker and GUI use options
igel at a glance
- Website
- github.com/nidhaloff/igel
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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igel FAQ
How do I specify what igel should do?
Write a yaml or json file that describes what you are trying to do.
Which input data formats are supported?
csv, txt, excel, json and html files, plus raw data stored in folders for auto-ml features.
How is igel installed?
Install with pip using: pip install -U igel
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