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Loud ML is the first open-source AI solution for ICT and IoT automation
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Loud ML - Reveal the hidden CircleCI Docker pulls

Loud ML is an open source inference engine for metrics and events, and the fastest way to embed machine learning in your time series application. This includes APIs for storing and querying data, processing it in the background for ML or detecting outliers for alerting purposes, and more.

You can use Loud ML as an AI bot that will enhance the management and operations of your most valuable assets through automation and prediction, for DevOps, for IoT, for energy, for logistics.

An Open-Source AI Library for Time Series Data

Loud ML is an open source time series inference engine built on top of TensorFlow. It's useful to forecast data, detect outliers, and automate your process using future knowledge.


  • Built-in HTTP API that facilitates the integration in other applications.
  • Data agnostic. The ML engine sits on top of all your data stores to provide instant results.
  • JSON like model feature specification.
  • Simple to install and manage, and fast to get data in and out.
  • Donut unsupervised learning model arXiv 1802.03903
  • It aims to process data in near real-time. That means data is queried at regular intervals and feed to the inference engine to return results.


We recommend installing Loud ML using one of the pre-built packages. Then start Loud ML using:

  • systemctl start loudmld if you have installed Loud ML using an official Debian or RPM package, and are running a distro with systemd.
  • loudmld and loudml if you have built Loud ML from source.

Local install

Inside a virtualenv:

make install

System-wide installation:

sudo make install

Getting Started

Running loudml command-line interface

loudml -c <path/to/configuration> <command>

See help for further information about commands

loudml -h

Running loudmld

loudmld -c <path/to/configuration>

Running unit tests

make test

Building Packages

make clean && make rpm
make clean && make repo



If you're feeling adventurous and want to contribute to Loud ML, see our contributing doc for info on how to make feature requests, build from source, and run tests.



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