Overview
The Neuralk SDK provides Python developers with a scikit-learn compatible interface to access Seldon, a foundation model specifically designed for tabular classification tasks.
Key Features:
- Zero hyperparameter tuning - Strong baseline performance out of the box
- Scikit-learn compatible - Works with pipelines, cross-validation, and familiar fit/predict interface
- Flexible deployment - Cloud API or on-premise server
- Mixed feature types - Handles numerical and categorical data
- Multiple model sizes - Choose between speed (seldon-flash) and accuracy (seldon-large)
Installation
pip install neuralk
Requirements: Python 3.11+
Quick Start
1. Get your API key
neuralk login
This displays instructions to create your account at prediction.neuralk-ai.com/register.
2. Set your API key
export NEURALK_API_KEY=nk_live_your_api_key_here
3. Make your first prediction
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from neuralk import SeldonClassifier
from neuralk.datasets import two_moons
# Load example dataset
X, y = two_moons()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# Create classifier and predict
clf = SeldonClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, predictions):.2%}")
Using with scikit-learn pipelines
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from skrub import TableVectorizer
from neuralk import SeldonClassifier
clf = make_pipeline(
TableVectorizer(),
SimpleImputer(),
SeldonClassifier(),
)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
On-premise deployment
from neuralk import SeldonClassifier
clf = SeldonClassifier(host="http://your-server:8000")
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
Available Models
| Model | Description |
|---|---|
seldon-flash |
Fastest model, optimized for low latency |
seldon-small |
Balanced model (default) |
seldon-large |
Most accurate, recommended for complex tasks |
clf = SeldonClassifier(model="seldon-large")
Documentation
- Getting Started Guide
- Understanding In-Context Learning
- Advanced Configuration
- Context Selection Strategies
- Example Gallery
Citation
If you use Neuralk in your research, please cite:
@software{neuralk2026sdk,
title = {Neuralk: A Foundation Model for Industrial Tabular Data},
author = {Neuralk AI},
year = {2026},
url = {https://www.neuralk-ai.com/}
}
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Contact
- Questions & Feature Requests: alex@neuralk-ai.com
- Collaborations: antoine@neuralk-ai.com
2 maintainers
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