ForML is a development framework for researching and implementing data science projects as well as an MLOps platform capable of managing their entire life cycles.
Use ForML to formally describe a data science problem as a composition of high-level operators. ForML expands your project into a task dependency graph specific to the given life-cycle phase and executes it using any of its supported technologies while taking care of all of its operational requirements.
Solutions built on ForML are naturally easy to reuse, extend, reproduce, or share and collaborate on.
Despite DAG (directed acyclic graph) being at the heart of ForML operations, it stands out among the many other task dependency processing systems due to its:
- Specialization in machine learning problems wired right into the flow topology.
- Concept of high-level operator composition helping to wrap complex ML techniques into simple reusable units.
- Abstraction of runtime dependencies allowing to implement fully portable projects that can be operated interchangeably using different technologies.
ForML started as a response addressing the notoriously painful process of transitioning any data science research into production. The framework was initially developed by a group of data scientists and ML engineers seeking to minimize the effort traditionally required to productionize any typical ML solution. Becoming increasingly useful to its original authors, ForML has been soon released as a community-driven project.