A development framework and MLOps platform for the lifecycle management of data science projects.
Project description
ForML
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:
- 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.
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
File details
Details for the file forml-0.93-py3-none-any.whl
.
File metadata
- Download URL: forml-0.93-py3-none-any.whl
- Upload date:
- Size: 283.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.10.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c51515aa7fc597c6b772c020f92fe2fbc7692c9394292ca24559e204e0cdd90f |
|
MD5 | becfb9e07eb6d290ddfd8c71973f3f48 |
|
BLAKE2b-256 | 7aedfd5113371942643ef22695ad605655c5269eac365105f79657c7d812050f |