| Name | niceml JSON |
| Version |
0.16.0
JSON |
| download |
| home_page | None |
| Summary | Welcome to niceML 🍦, a Python-based MLOps framework that uses TensorFlow and Dagster. This framework streamlines the development, and maintenance of machine learning models, providing an end-to-end solution for building efficient and scalable pipelines. |
| upload_time | 2024-06-13 09:44:40 |
| maintainer | None |
| docs_url | None |
| author | Denis Stalz-John |
| requires_python | !=2.7.*,!=3.0.*,!=3.1.*,!=3.12.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*,>=3.9 |
| license | None |
| keywords |
tensorflow
scikit-learn
streamlit
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# This is the readme for niceML
[](
https://pypi.org/project/niceml/
)
[](
https://github.com/codecentric-oss/niceml/actions/workflows/pytest.yaml)
[](
https://opensource.org/licenses/MIT)
**niceML** is a tool to help you set up your machine learning projects faster.
It provides pipelines for a variety of ML tasks, like
- **Object Detection**,
- **Semantic Segmentation**,
- **Regression**,
- **Classification**
- and others.
All you have to do is configure your pipeline, and you're ready to go!
You can also add your own components to the build-in dashboard,
where you can compare the results and performance of your ML models.
Further documentation is available at [niceML.io](https://niceml.io).
A lot more documentation will follow soon!
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