deeploi 0.1.1

One-line deployment for trained tabular ML models

Deeploi — Deploy ML Models in Seconds 🚀

Turn your trained tabular ML model into a production-ready API with a single line of code. No DevOps. No boilerplate. No headaches.

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Why Deeploi?

• Instant API: Serve your scikit-learn or XGBoost model as a blazing-fast REST API in one command.
• Zero Config: No YAML, no Docker, no cloud lock-in. Just your model and your data.
• For Data Scientists: Focus on modeling, not infrastructure.
• For Startups & Teams: Ship ML features faster, without waiting for MLOps.
• Local-First: Run anywhere—laptop, server, or cloud VM.

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Get Started in 10 Seconds

from deeploi import deploy

deploy(model, sample=X_train)

Your model is now live at http://127.0.0.1:8000 with /predict, /health, and /meta endpoints.

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Features

• ✅ One-line deployment for tabular models
• ✅ Automatic schema inference from your training data
• ✅ Model packaging & versioning for reproducibility
• ✅ Prediction probabilities for classifiers
• ✅ Supports scikit-learn & XGBoost
• ✅ No cloud or container required
• ✅ Optional Docker artifact generation

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Quick Start

pip install deeploi

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from deeploi import deploy

iris = load_iris(as_frame=True)
X, y = iris.data, iris.target
model = RandomForestClassifier().fit(X, y)
deploy(model, sample=X)

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How It Works

1. Train your model as usual.
2. Deploy instantly with deploy(model, sample=X_train).
3. Call your API for predictions.

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API Endpoints

• POST /predict — Get predictions
• POST /predict_proba — Get class probabilities (classifiers)
• GET /meta — Model metadata
• GET /health — Health check

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Save, Load, and Reuse

from deeploi import package, load

pkg = package(model, X_train)
pkg.save("artifacts/v1")
pkg = load("artifacts/v1")
preds = pkg.predict(X_test)

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Who is Deeploi for?

• Data scientists who want to demo or share models instantly
• ML engineers who need fast, local serving for tabular models
• Startups and teams who want to skip MLOps complexity

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Advanced Usage

Three Core Functions

1. deploy() — One-liner, immediate serving

from deeploi import deploy

deploy(model, sample=X_train, host="127.0.0.1", port=8000)

2. package() — Reusable object

from deeploi import package

pkg = package(model, sample=X_train)
preds = pkg.predict(X_test)
pkg.save("artifacts/iris_rf")
pkg.serve(port=8000)

3. load() — Reload saved artifacts

from deeploi import load

pkg = load("artifacts/iris_rf")
preds = pkg.predict(X_test)

4. Docker Generation — Containerize a saved artifact

from deeploi import package

pkg = package(model, sample=X_train)
pkg.save("artifacts/iris_rf", generate_docker=True)

# or generate Docker files later
pkg.generate_docker("artifacts/iris_rf", port=8000)

Build and run:

cd artifacts/iris_rf
docker build -t iris-model .
docker run --rm -p 8000:8000 iris-model

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Example: Test the API

curl -X POST http://127.0.0.1:8000/predict \
  -H "Content-Type: application/json" \
  -d '{
    "records": [
      {
        "sepal length (cm)": 5.1,
        "sepal width (cm)": 3.5,
        "petal length (cm)": 1.4,
        "petal width (cm)": 0.2
      }
    ]
  }'

Response:

{
  "predictions": [0],
  "probabilities": [
    {"0": 0.91, "1": 0.09}
  ]
}

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Artifact Structure

When you call pkg.save("path/to/artifact"), you get:

path/to/artifact/
├── model.joblib           # Serialized model
├── metadata.json          # Versions, task type, timestamps
├── schema.json            # Features, dtypes, column order
├── deeploi.json           # Manifest
└── requirements.txt       # Dependencies

# Optional (if Docker generation is enabled)
path/to/artifact/
├── Dockerfile             # Container build definition
├── .dockerignore          # Ignore unnecessary build files
└── serve.py               # ASGI entrypoint for uvicorn

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Error Handling

from deeploi import package, UnsupportedModelError, InvalidSampleError

try:
    pkg = package(my_model, my_sample)
except UnsupportedModelError:
    print("Only sklearn and XGBoost are supported")
except InvalidSampleError:
    print("Sample must be a non-empty DataFrame")

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What's in v0.1.0

Supported:

• sklearn classifiers and regressors
• XGBoost classifiers and regressors
• pandas DataFrame inputs
• Local FastAPI serving
• Model save/load
• Schema inference
• Prediction probabilities (classifiers)
• Docker file generation for saved artifacts

Not in v0.1.0:

• S3 / cloud storage
• Authentication
• Batch inference
• Async workers
• Model registry
• Monitoring

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Requirements

• Python 3.8+
• pandas >= 1.0
• scikit-learn >= 0.24
• xgboost >= 1.0
• fastapi >= 0.68
• uvicorn >= 0.15

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License

MIT — see LICENSE.

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Ready to deploy your model?
Try the examples or run pip install deeploi now!

Questions? Open an issue on GitHub.