Enterprise Machine-Learning and Predictive Analytics
Nemoa is a machine learning- and data analysis framework, that implements the Cloud-Assisted Meta Programming (CAMP) paradigm.
The key goal of Nemoa is to provide a long-term data analysis framework, which seemingly integrates into existing enterprise data environments and thereby supports collaborative data science. To achieve this goal Nemoa orchestrates established Python frameworks like TensorFlow® and SQLAlchemy and dynamically extends their capabilities by community driven algorithms (e.g. for probabilistic graphical modeling, machine learning and structured data-analysis).
Thereby Nemoa allows client-side implementations to use abstract currently best fitting (CBF) algorithms. During runtime the concrete implementation of CBF algorithms are chosen server-sided by category and metric. An example for such a metric would be the average prediction accuracy within a fixed set of gold standard samples of the respective domain of application (e.g. latin handwriting samples, spoken word samples, TCGA gene expression data, etc.).
Current Development Status
Nemoa currently is in Pre-Alpha development stage, which immediately follows the Planning stage. This means, that at least some essential requirements of Nemoa are not yet implemented.
Comprehensive information and installation support is provided within the online manual. If you already have a Python environment configured on your computer, you can install the latest distributed version by using pip:
$ pip install nemoa
Contributors are very welcome! Feel free to report bugs and feature requests to the issue tracker provided by GitHub. Currently, as the Frootlab Developers team still is growing, we do not provide any Contribution Guide Lines to collaboration partners. However, if you are interested to join the team, we would be glad, to receive an informal application.
© 2019 Frootlab Developers: Patrick Michl <email@example.com> © 2013-2019 Patrick Michl
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