mlsentinel 0.1.3.dev3

Python SDK for monitoring and validating Machine Learning models.

MLSentinel

Validate and submit machine-learning evaluation metrics from Python.

MLSentinel is a lightweight Python SDK for sending model evaluation reports to the MLSentinel platform. It validates report data locally, authenticates requests with your API key, and returns the platform response as JSON.

Developer preview — v0.1.3.dev3

This pre-release focuses on reliable report submission: local validation, HTTP transport, API-key authentication, and actionable SDK errors. Analysis and dashboard features are planned for later releases.

Installation

MLSentinel supports Python 3.9 and later.

pip install mlsentinel

For this development build, install from a local checkout:

pip install .

Quick start

Create an API key in the MLSentinel platform, then submit a report:

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())  # 0.1.3.dev3

Keep API keys out of source control. In production, load the key from your secret manager or environment rather than embedding it in code.

What happens when you submit a report

Your application
      │
      ▼
MLDoc.doc_report(...)
      │
      ├─ Local input validation
      │
      └─ HTTP request with X-API-Key
                    │
                    ▼
            MLSentinel API
                    │
                    ▼
            JSON response or SDK exception

Supported metrics and validation

All reports require a non-empty string for both project and model. The metrics argument must be a non-empty dictionary containing only the supported metrics below.

Metric	Accepted value
accuracy	Number from 0 to 1
precision	Number from 0 to 1
recall	Number from 0 to 1
f1_score	Number from 0 to 1
roc_auc	Number from 0 to 1
val_loss	Number greater than or equal to 0

Validation occurs before a network request is made, helping you catch malformed reports early.

Error handling

MLSentinel raises SDK-specific exceptions with stable error codes. Catch the common base class when you want one error path for validation, authentication, network, and server failures.

from mlsentinal import MLDoc
from mlsentinal.exceptions import MLSentinalError

client = MLDoc("YOUR_API_KEY")

try:
    client.doc_report(
        project="Spam Detector",
        model="Random Forest",
        metrics={"accuracy": 1.2},
    )
except MLSentinalError as error:
    print(error.code)     # for example: MLS026
    print(error.message)  # accuracy must be between 0 and 1.

Category	Exception
Invalid project, model, or metrics	ProjectValidationError, ModelValidationError, MetricValidationError
Invalid API key	InvalidAPIKeyError
Authentication or authorization failure	AuthenticationError
Timeout, connection, or request error	MLSentinalConnectionError
Unexpected API response or server error	MLSentinalServerError

Version 0.1.3.dev3

This development release adds and improves:

• Standardized SDK error codes for validation, network, authentication, and server failures.
• Clearer exception messages for unsupported and invalid metric values.
• HTTP report transport with API-key authentication through the X-API-Key header.
• Timeout and connection-error handling.
• Local validation of projects, models, and supported evaluation metrics.

API reference

MLDoc(api_key)

Creates a client authenticated with the supplied API key.

client.doc_report(project, model, metrics)

Validates and submits a model report. On success, it returns the JSON body returned by the MLSentinel API. On failure, it raises an MLSentinalError subclass.

client.version()

Returns the installed SDK version.

Requirements

• Python 3.9+
• requests 2.31.0+

Roadmap

• AI-assisted model analysis and health reports
• Performance recommendations and diagnostics
• Dashboard and report history
• Model monitoring and drift detection
• CI/CD integrations

Contributing

Contributions are welcome. Please open an issue to discuss significant changes, then submit a focused pull request with tests or examples where appropriate.

License

MLSentinel is distributed under the MIT License.

Author

Created by Adari Narasimha Dhoni.