ejhusom/d2m

A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).

28
/ 100
Experimental

This tool helps data scientists and machine learning engineers transform raw tabular or time-series data into fully trained and evaluated machine learning models. You provide your dataset, define your target variable, and the system outputs a model with built-in explanations, uncertainty estimates, and carbon emission reports. It's designed for practitioners who need to develop responsible AI solutions.

No commits in the last 6 months.

Use this if you need to quickly build and evaluate explainable and environmentally conscious machine learning models from tabular or time series data.

Not ideal if you're working with image, text, or other unstructured data types, or if you require deep customization of the model architecture.

data-science machine-learning-engineering responsible-AI data-analysis predictive-modeling
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 8 / 25

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Stars

8

Forks

1

Language

Python

License

MIT

Last pushed

Feb 20, 2024

Commits (30d)

0

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