machine-learning-process

Machine Learning Process using the EO Application Package

https://github.com/eoap/machine-learning-process

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
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Academic email domains
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (9.9%) to scientific vocabulary

Keywords

common-workflow-language inference machine-learning training

Last synced: 4 months ago · JSON representation

Repository

Machine Learning Process using the EO Application Package

Basic Info

Host: GitHub
Owner: eoap
License: apache-2.0
Language: Jupyter Notebook
Default Branch: main
Homepage: https://eoap.github.io/machine-learning-process/
Size: 147 MB

Statistics

Stars: 0
Watchers: 2
Forks: 2
Open Issues: 0
Releases: 2

Topics

common-workflow-language inference machine-learning training

Created 10 months ago · Last pushed 4 months ago

Metadata Files

Readme License Codemeta

README.md

Machine-learning-process

Users should follow the documentation available at this link.

The documentation is organized into sections, each providing specific information about the project:

Introduction: Offers a high-level overview of the project.
How-to Guides: Outlines the order in which modules should be executed.
Tutorials: Walks through the steps required to run the application packages:
- Application Packages:
- Run the Training Module using CWL Runner
- Run the Inference Module using CWL Runner
- Describe a Trained Machine Learning Model
Reference Guides: Provides in-depth documentation for the two application packages:
- Training a Machine Learning Model
- Running Inference on Real-World Data
Technical Insights and Learnings: Shares the decisions and insights gained during development.

Owner

Name: Earth Observation Application Package
Login: eoap
Kind: organization
Location: Italy

Repositories: 1
Profile: https://github.com/eoap

Best practices and examples to package Earth Observation applications

CodeMeta (codemeta.json)

{
  "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
  "@type": "SoftwareSourceCode",
  "license": "https://spdx.org/licenses/CC-BY-NC-SA-4.0",
  "codeRepository": "https://github.com/eoap/machine-learning-process.git",
  "dateCreated": "2025-03-19",
  "datePublished": "2025-03-19",
  "dateModified": "2025-03-19",
  "name": "Tile-based Classification",
  "version": "0.0.4",
  "description": "Tile-based Classification",
  "developmentStatus": "active",
  "relatedLink": [
    ""
  ],
  "funder": {
    "@type": "Organization",
    "name": "Terradue"
  },
  "keywords": [
    "NDWI",
    "Landsat-9",
    "Sentinel-2",
    "Tile based Classification"
  ],
  "programmingLanguage": [
    "Python",
    "CWL"
  ],
  "softwareRequirements": [
    "container runtime",
    "cwl runner",
    "mlflow"
  ],
  "author": [
    {
      "@type": "Person",
      "givenName": "Parham",
      "familyName": "Membari",
      "email": "parham.membari@terradue.com",
      "affiliation": {
        "@type": "Organization",
        "name": "Terradue"
      }
    }
  ]
}

GitHub Events

Total

Release event: 3
Delete event: 1
Member event: 1
Issue comment event: 2
Push event: 83
Pull request review comment event: 1
Pull request event: 2
Fork event: 4
Create event: 5

Last Year

Release event: 3
Delete event: 1
Member event: 1
Issue comment event: 2
Push event: 83
Pull request review comment event: 1
Pull request event: 2
Fork event: 4
Create event: 5

Dependencies

.github/workflows/build.yaml actions

actions/checkout v2 composite
actions/create-release v1 composite
actions/setup-python v2 composite
actions/upload-artifact v4 composite
actions/upload-release-asset v1 composite

.github/workflows/docs.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

inference/make-inference/Dockerfile docker

rockylinux 9.3-minimal build

MLM/pyproject.toml pypi

inference/make-inference/pyproject.toml pypi

inference/make-inference/setup.py pypi

training/make-ml-model/Dockerfile docker

rockylinux 9.3-minimal build

inference/make-inference/environment.yml pypi

click *
georeader-spaceml *
loguru *
onnx *
onnxmltools *
onnxruntime *
rasterio *
rio_stac *
tf2onnx *
tqdm *

training/make-ml-model/pyproject.toml pypi

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science