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Integration guides

Api

Hosted API

Get started in minutes. Create an account, grab your API key, and run predictions from anywhere. No infrastructure to set up — we handle the GPUs, the scaling, and the availability.

from neuralk import Seldon


clf = Seldon(api_key="nk_live_xxx")
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
Deployment

On-premise deployment

For organizations that require full data sovereignty, Neuralk deploys on your infrastructure. Your data never leaves your perimeter. Same SDK, same developer experience — just point it to your own endpoint. Compatible with Kubernetes, private cloud, and bare-metal environments.

from neuralk import Seldon
clf = Seldon("https://seldon.internal.your-company.com)")

clf.fit(X_train, y_train)

predictions = clf.predict(X_test)
Connection

Connect your data

Neuralk works with the data tools you already use. Load your datasets from any source, pass them to the classifier — we handle the rest.

  • DataFrames

    Pandas

    Polars

  • Arrays

    NumPy

  • File formats

    Parquet

    CSV

    Excel

  • Cloud storage

    Amazon S3

    Google Cloud Storage

    Azure Blob Storage

  • Data warehouses

    Snowflake

    BigQuery

    Redshift

    Databriks

  • Databases

    PostgreSQL

    MySQL

    Any ODBC/JDBC source

import pandas as pd
from neuralk import Seldon


# Load from wherever your data lives
df_train = pd.read_parquet("s3://my-bucket/train.parquet")
df_test = pd.read_parquet("s3://my-bucket/test.parquet")
clf = Seldon(api_key="nk_live_xxx")
clf.fit(df_train[features], df_train[target])
predictions = clf.predict(df_test[features])
Compatibility

Scikit-learn compatible

Neuralk implements the scikit-learn estimator interface. It plugs directly into your existing ML pipelines — cross-validation, grid search, pipelines, and model selection all work out of the box.

from sklearn.model_selection import cross_val_score
from neuralk import Seldon


clf = Seldon(api_key="nk_live_xxx")
scores = cross_val_score(clf, X, y, cv=5)