Evidence tier Source Confirmed · 9 captures on record
What the publisher says
As described on Microsoft Marketplace.
Ludwig is an open-source, no-code machine learning toolbox built in Python that enables users to develop, train, evaluate, and deploy deep learning models with minimal coding. It provides a declarative, configuration-driven approach to machine learning, allowing users to work with structured data, text, images, audio, time series, and other data types. Ludwig automates data preprocessing, feature engineering, model training, hyperparameter optimization, and evaluation, making it suitable for both beginners and experienced machine learning practitioners.
Features of Ludwig:
Show the rest of the publisher’s description (16 more lines)
- No-code, configuration-based workflow for building machine learning and deep learning models.
- Supports multiple data types, including tabular data, text, images, audio, sequences, and time series.
- Automatically performs data preprocessing, feature encoding, model training, and evaluation.
- Built on modern deep learning frameworks with optional GPU acceleration for faster model training.
- Supports hyperparameter optimization, experiment tracking, and model explainability.
- Provides built-in support for distributed training, inference, and model export for production deployment.
- Extensible architecture that allows users to customize encoders, decoders, trainers, and model configurations.
- Open-source under the Apache License 2.0 with active community development and comprehensive documentation.
To verify the working of Ludwig in your shell:
$ sudo su
$ cd /opt/ludwig
$ source venv/bin/activate
$ ludwig version
# If the command above is unavailable:
$ python -m ludwig version
Disclaimer: Ludwig is an open-source machine learning framework released under the Apache License 2.0. It is maintained by the Ludwig open-source community. Users are responsible for validating models, datasets, and configurations before deploying them in production environments. Always consult the official Ludwig documentation for the latest installation instructions, supported features, and best practices.
Agent build and provenance
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The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment · as of 2026-08-29
Sources
Publisher resources
1 linkLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.