MLSentinel SDK

MLSentinel SDK is the Python client for sending model evaluation reports and data-quality summaries to the MLSentinel platform. It keeps the workflow simple: validate locally, send with your API key, and get back a structured JSON response or a clear SDK error.

Installation

The SDK supports Python 3.9 and newer.

Install from PyPI:

pip install mlsentinel

Or install from a local checkout while developing:

pip install .

Quick Start

from mlsentinal import MLDoc

client = MLDoc("YOUR_API_KEY")

response = client.doc_report(
    project="Spam Detector",
    model="Random Forest",
    metrics={
        "accuracy": 0.95,
        "precision": 0.94,
        "recall": 0.93,
        "f1_score": 0.935,
        "roc_auc": 0.98,
        "val_loss": 0.18,
    },
)

print(response)
print(client.version())

Keep API keys out of source control. In real projects, read them from environment variables or a secret manager instead of hard-coding them.

What the SDK Does

MLSentinel SDK is designed to stay out of your way:

• validates project, model, and metrics before making a network request
• sends reports to the backend using the X-API-Key header
• returns the API response as JSON when the request succeeds
• raises SDK-specific exceptions when validation, authentication, network, or server handling fails
• can also generate and upload a data-quality summary from a pandas DataFrame

Data Quality Reports

If you want a quick health snapshot of a dataset, the SDK can summarize it locally and send the result to the platform.

import pandas as pd
from mlsentinal import MLDoc

df = pd.read_csv("creditcard.csv")

client = MLDoc("YOUR_API_KEY")
response = client.report_data_quality(
    project="Loan Prediction",
    model="ResNet50",
    dataframe=df,
)

print(response)

This feature requires pandas and numpy.

Validation Rules

Every report expects a non-empty string for both project and model. The metrics argument must be a non-empty dictionary, and only the supported metrics below are accepted.

Validation happens locally first, so malformed payloads are caught before a request leaves your machine.

Errors

The SDK raises its own exception types so you can handle failures cleanly in one place.

from mlsentinal import MLDoc
from mlsentinal.exceptions import MLSentinelError

client = MLDoc("YOUR_API_KEY")

try:
    client.doc_report(
        project="Spam Detector",
        model="Random Forest",
        metrics={"accuracy": 1.2},
    )
except MLSentinelError as error:
    print(error.code)
    print(error.message)

API Reference

MLDoc(api_key, check_version=True)

Creates a client for the MLSentinel platform. The client can optionally check SDK compatibility during initialization.

client.doc_report(project, model, metrics)

Validates and submits a model report. On success, it returns the JSON response from the backend.

client.report_data_quality(project, model, dataframe)

Builds a local data-quality summary and submits it to the backend.

client.version()

Returns the installed SDK version.

Requirements

• Python 3.9+
• requests 2.31.0+

License

MLSentinel is distributed under the MIT License.

Author

Created by Adari Narasimha Dhoni.