Easy-ML Studio

Easy-ML Studio is a no-code machine learning studio that runs on your own machine. Upload a dataset in the browser, train several models side by side, compare their metrics, make predictions, explain them with SHAP, and export a trained model as a ready-to-run FastAPI service.

It covers three workflows:

• Tabular classification and regression (CSV / Excel)
• Text classification (TF-IDF or sentence embeddings)
• Image classification (SimpleCNN, ResNet18, MobileNetV2, or your own CNN)

Everything stays local: the UI, the models and your data never leave your computer.

Install

pip install easy-ml-studio

The core install covers tabular ML with scikit-learn models and TF-IDF text classification. Add extras for more:

pip install "easy-ml-studio[all]"
easy-ml download-models   # spaCy English model, needed for lemmatization

Python 3.10 – 3.12 is supported. Features whose extra is not installed are greyed out in the UI with the command to enable them.

Launch

easy-ml

The studio opens in your browser at http://127.0.0.1:5010. Useful options:

easy-ml --port 8080          # use another port
easy-ml --no-browser         # don't open a browser tab
easy-ml --host 0.0.0.0       # expose on your network (see Security below)
python -m easyml             # same as easy-ml

Features

• Train several models in one run and compare accuracy, confusion matrices and classification reports
• Light or thorough hyperparameter search (GridSearchCV)
• Missing-value imputation, scaling, PCA, one-hot encoding, and SMOTE / Tomek Links for imbalanced classes
• Live progress and training curves while models train; stop a run at any time
• Run history stored in SQLite, so every model can be reused later
• Single-record and batch (CSV) predictions
• SHAP waterfall explanations for tabular predictions
• One-click export of a tabular model as a FastAPI app with Dockerfile
• Automatic EDA reports with ydata-profiling

Use it from Python

from easyml import MLTrainer

run = MLTrainer(
    task="MLClassification",          # or "MLRegression", "NLPTextClassification"
    name="churn",
    dataset_path="churn.csv",
    target_column="churned",
    models=["logistic_regression", "random_forest"],
).run()

print(run.run_id, run.performance)

Runs trained from Python appear in the studio's Run History and can be used for predictions there.

Where data is stored

Uploads, trained models, the run database and logs live outside the package, so upgrading never deletes them:

Set the EASYML_DATA_DIR environment variable to use another folder. Settings → Clear Data in the studio deletes everything stored there.

Security

Easy-ML Studio is a local, single-user tool with no login.

• By default it only listens on 127.0.0.1, so other machines cannot reach it.
• Requests from other websites are rejected, so a page you visit cannot drive the studio.
• Only use --host 0.0.0.0 on a network you trust: anyone who can reach the port can use the studio and its data.

Docker

docker compose up --build

The image targets base, ml, nlp, cv and full (default) select which extras are installed. Data is kept in the easyml-data volume.

Documentation

• User guide
• Developer guide

Development

git clone https://github.com/Yatrik07/easy-ml.git
cd easy-ml
python -m venv venv
venv\Scripts\activate            # Windows; use `source venv/bin/activate` on macOS/Linux
pip install -e ".[all,dev]"
pytest
easy-ml

License

Apache License 2.0