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 loudmldif you have installed Loud ML using an official Debian or RPM package, and are running a distro with
loudmlif you have built Loud ML from source.
Inside a virtualenv:
sudo make install
Running loudml command-line interface
loudml -c <path/to/configuration> <command>
See help for further information about commands
loudmld -c <path/to/configuration>
Running unit tests
make clean && make rpm
make clean && make repo
- Read more about the design goals and motivations of the project.
- Follow the getting started guide to learn the basics in just a few minutes.
- Learn more about Loud ML's key concepts.
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.
Looking for Support?
Contact email@example.com to learn how we can best help you succeed.