Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way.
The platform provides the abstractions and declarative capabilities for data extraction & feature engineering followed by model training and serving. Liminal's goal is to operationalize the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validation, deployment and inference in production, freeing them from engineering and non-functional tasks, and allowing them to focus on machine learning code and artifacts.
Using simple YAML configuration, create your own schedule data pipelines (a sequence of tasks to perform), application servers, and more.
A simple hello world guide for Liminal can be found here
A more advanced example which demonstrates a simple data-science workflow can be found [here](getting-started/iris_classification.md
Full documentation of Apache Liminal can be found here
High level architecture documentation can be found here