Is Azureml Datadrift Safe?
Azureml Datadrift — Nerq Trust Score 67.2/100 (B- grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-05-06.
Use Azureml Datadrift with some caution. Azureml Datadrift is a Python package with a Nerq Trust Score of 67.2/100 (B-), based on 3 independent data dimensions. Below the recommended threshold of 70. Security: 90/100. Popularity: 30/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Azureml Datadrift safe?
CAUTION — Azureml Datadrift has a Nerq Trust Score of 67.2/100 (B-). It has moderate trust signals but shows some areas of concern that warrant attention. Suitable for development use — review security and maintenance signals before production deployment.
What is Azureml Datadrift's trust score?
Azureml Datadrift has a Nerq Trust Score of 67.2/100, earning a B- grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Azureml Datadrift?
Azureml Datadrift's strongest signal is security at 90/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Azureml Datadrift and who maintains it?
| Author | Microsoft Corp |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Azureml Datadrift
What is Azureml Datadrift?
Azureml Datadrift is a Python package — Contains functionality for data drift detection for various datasets used in machine learning..
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=azureml-datadrift
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Azureml Datadrift has a Nerq Trust Score of 67/100 (B-) and has not yet reached Nerq trust threshold (70+). This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Azureml Datadrift has a Trust Score of 67/100 (B-).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 100/100 |
| Popularity | 30/100 |
| Quality | 50/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Azureml Datadrift collect?
Privacy assessment for Azureml Datadrift is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Azureml Datadrift secure?
Security score: 90/100. Azureml Datadrift has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under https://aka.ms/azureml-sdk-license, allowing code inspection. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Azureml Datadrift Security Report
How we calculated this score
Azureml Datadrift's trust score of 67.2/100 (B-) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (100/100), popularity (30/100), quality (50/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on May 06, 2026. Data version: 0.0.
Full methodology documentation · Machine-readable data (JSON API)
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.