Is Bigquery Ml Utils Safe?
Bigquery Ml Utils — Nerq Trust Score 63.0/100 (C+ grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-04-26.
Use Bigquery Ml Utils with some caution. Bigquery Ml Utils is a Python package with a Nerq Trust Score of 63.0/100 (C+), 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 Bigquery Ml Utils safe?
CAUTION — Bigquery Ml Utils has a Nerq Trust Score of 63.0/100 (C+). 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 Bigquery Ml Utils's trust score?
Bigquery Ml Utils has a Nerq Trust Score of 63.0/100, earning a C+ grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Bigquery Ml Utils?
Bigquery Ml Utils'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 Bigquery Ml Utils and who maintains it?
| Author | Google Inc. |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Bigquery Ml Utils
What is Bigquery Ml Utils?
Bigquery Ml Utils is a Python package — BigQuery ML Utils.
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=bigquery-ml-utils
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Bigquery Ml Utils has a Nerq Trust Score of 63/100 (C+) 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
- Bigquery Ml Utils has a Trust Score of 63/100 (C+).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 79/100 |
| Popularity | 30/100 |
| Quality | 55/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Bigquery Ml Utils collect?
Privacy assessment for Bigquery Ml Utils is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Bigquery Ml Utils secure?
Security score: 90/100. Bigquery Ml Utils has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under Apache 2.0, 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: Bigquery Ml Utils Security Report
How we calculated this score
Bigquery Ml Utils's trust score of 63.0/100 (C+) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (79/100), popularity (30/100), quality (55/100), 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 April 26, 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.