MLDB (Machine Learning Database) is an open-source, SQL-based software project designed for machine-learning workflows. Its documented approach connects datasets, training procedures and model-backed functions, which can be called from SQL or exposed through REST endpoints. The important qualification is that the project’s repository says its former Enterprise Edition, Docker Containers and Hub are no longer maintained; the hosted documentation describes the last commercial release, not a current supported deployment.
What is MLDB?
MLDB is a database project built around machine-learning tasks rather than a general-purpose database with an unrelated model service bolted on. Its interface uses SQL, while its documented workflow represents data as datasets, work as procedures and reusable model or expression logic as functions. The project is hosted at github.com/mldbai/mldb, where it is identified as the Machine Learning Database and described as an open-source research project.
The repository says MLDB was developed by MLDB.ai, which was sold to Element AI in 2017. It characterizes later work as a small spare-time open-source research project. That history and the explicit warning about unmaintained former products are material when deciding whether to evaluate MLDB for a new system.
How does MLDB’s machine-learning workflow work?
The archived official overview describes a sequence that moves from data to a callable model. It is documentation for the last commercial release, which the repository says is out of date but generally helpful; it should not be read as proof of current production support.
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- Load data into a dataset. A dataset holds named data points and their data. The documentation also describes file-backed datasets and files reached through URLs, including protocols such as S3 and HDFS.
- Run a procedure. Procedures perform batch operations, including transforming or cleaning data, training models, and applying models to data in batch.
- Configure a function from the model output. Functions can encapsulate SQL expressions or apply trained models. A function makes the model or expression reusable in later queries or scoring.
- Score records. Use the function in SQL for querying or batch scoring, or expose it as a REST endpoint for real-time scoring.
The docs also sketch multiple MLDB instances sharing storage, with separate machines handling collection, training or scoring. This is an architecture described in the historical documentation, not a present-day deployment recommendation.
How can MLDB serve a machine-learning model?
The documented workflow offers two ways to invoke scoring. Batch SQL scoring fits work performed over a dataset; REST scoring fits an application that sends requests to a function endpoint. The source material establishes these as documented capabilities, but does not establish that either route is currently supported as a maintained production service.
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| Documented route | How it works | Best understood as |
|---|---|---|
| SQL | A function is called from SQL and can be applied to data, including in batch. | Query-driven or dataset-scale scoring described in the archived overview. |
| REST endpoint | A function can be exposed for calls over REST. | Request-based scoring described in the archived overview. |
Is MLDB still maintained?
The current repository explicitly says the former MLDB Enterprise Edition, MLDB Docker Containers and MLDB Hub are no longer maintained and advises against using them. It describes the remaining project as spare-time open-source research work, so its statements do not amount to a release schedule, support commitment or guarantee for a particular platform. Check the repository for the latest status before adopting it.
The repository also labels the hosted documentation as documentation for the last commercial release that is out of date, though generally helpful. Use it to understand the design and historical workflow, not as confirmation that old packaged distributions or every documented feature remain supported.
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How do you install MLDB?
The repository says building MLDB from source is the way to obtain an up-to-date version. It states that MLDB can be built and run on Linux or macOS on Intel, ARM or Apple processors. It does not establish compatibility for a specific operating-system version, a current prebuilt installer, or a supported production setup; consult the repository’s build instructions and current issue tracker before committing to an environment.
Do not treat the former Docker Containers or Enterprise Edition as current installation shortcuts: the project marks them unmaintained. The repository points users with questions to GitHub issues or Gitter, while noting contributors work in their spare time.
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Is MLDB open source, and what license applies?
The repository identifies MLDB as Apache License 2.0 software, with a caveat that material in the ext directory may have separate compatible licenses. Review the applicable license files for the parts you intend to use. An archived licensing page includes historical Enterprise Edition terms, but those terms do not establish a currently maintained commercial edition or current support offer.
Should you consider MLDB for a new project?
MLDB is most straightforward to understand as an open-source project with an interesting SQL-centered design for managing data, model training and scoring in one workflow. Its current status is a substantial practical constraint: the maintainers’ repository warning rules out relying on the former packaged Enterprise, container or Hub offerings as maintained products, and its build guidance does not promise a support lifecycle.
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- For learning or investigation: the archived overview can help explain the datasets–procedures–functions model, provided its age and status are kept in view.
- For a new production system: verify that you can build and maintain the software yourself, that your target platform works, and that the project’s current state meets your operational requirements. The available project statements do not establish a support commitment.
- For evaluation: distinguish current repository guidance from historical documentation, and avoid basing a deployment plan on old prebuilt distributions.
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