https://github.com/formlio/forml

ForML - A development framework and MLOps platform for the lifecycle management of data science projects
https://github.com/formlio/forml

ai data-science machine-learning ml mlops portability python reproducibility

Last synced: 4 months ago
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ForML - A development framework and MLOps platform for the lifecycle management of data science projects

• Host: GitHub
• URL: https://github.com/formlio/forml
• Owner: formlio
• License: apache-2.0
• Created: 2020-11-04T17:04:13.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-05-29T10:58:22.000Z (about 1 year ago)
• Last Synced: 2024-03-19T05:04:11.051Z (4 months ago)
• Topics: ai, data-science, machine-learning, ml, mlops, portability, python, reproducibility
• Language: Python
• Homepage: http://forml.io
• Size: 6.32 MB
• Stars: 105
• Watchers: 9
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-stars - formlio/forml - Lifecycle management framework for Data science projects (Python)
• awesome-workflow-engines - ForML - A development framework and MLOps platform for the lifecycle management of data science projects. (Full fledged product)

README

        


ForML

=====

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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.

Not Just Another DAG

--------------------

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:

1. Specialization in machine learning problems wired right into the flow topology.

2. Concept of high-level operator composition helping to wrap complex ML techniques into simple

   reusable units.

3. Abstraction of runtime dependencies allowing to implement fully portable projects that can be

   operated interchangeably using different technologies.

History

-------

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 released as a community-driven project.

Resources

---------

* [Documentation](https://docs.forml.io/en/latest/)

* [Source Code](https://github.com/formlio/forml/)

* [Chat Room](https://app.gitter.im/#/room/#formlio_community:gitter.im)

* [Issue Tracker](https://github.com/formlio/forml/issues/)

* [PyPI Repository](https://pypi.org/project/forml/)