AI Analysis
Final verdict: SUSPICIOUS
The package shows low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to the author's new or inactive account status.
- Low risk in technical aspects
- Elevated metadata risk due to author's account status
Per-check LLM notes
- Network: No network calls detected, which is normal for a metrics package that does not require external communication.
- Shell: No shell execution patterns detected, aligning with the expected behavior of a legitimate package.
- Obfuscation: No obfuscation patterns detected, suggesting low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has a new or inactive account with only one package, which could indicate a less established presence and potential risk.
Package Quality Overall: Low (2.4/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (598 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Microsoft Corp" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with azureml-metrics
Create a mini-application that monitors and evaluates machine learning models deployed on Azure Machine Learning Service. This application will use the 'azureml-metrics' package to gather performance metrics and visualize them in real-time. Here’s a detailed breakdown of what your application should do: 1. **Setup**: Begin by setting up your environment. Ensure you have Azure Machine Learning Workspace access and the necessary permissions. Install the required packages including 'azureml-metrics', 'azureml-core', and 'pandas'. 2. **Model Deployment**: Deploy a pre-trained machine learning model to Azure ML Service using Azure SDK. For simplicity, let’s assume the model predicts a continuous value, like housing prices. 3. **Metric Collection**: Use 'azureml-metrics' to collect performance metrics from the deployed model. Metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared should be collected over time. Explain how these metrics are relevant to evaluating the model's performance. 4. **Data Visualization**: Implement a feature where the collected metrics are visualized in real-time. Utilize libraries like Matplotlib or Plotly to create dynamic graphs that update as new data comes in. Discuss how real-time visualization aids in quick decision-making regarding model performance. 5. **Alert System**: Integrate an alert system that notifies users when certain thresholds are breached in the metric values. For instance, if the MAE exceeds a certain threshold, send an email notification. Discuss how this feature helps in maintaining model reliability. 6. **User Interface**: Develop a simple web interface using Flask or Django where users can view the current metrics and historical trends. This UI should also allow users to set custom thresholds for alerts. 7. **Documentation and Testing**: Write comprehensive documentation explaining each part of the application. Also, include unit tests for critical components of your application to ensure robustness. By following these steps, you'll create a powerful tool for monitoring and maintaining the performance of machine learning models in production, leveraging the capabilities of the 'azureml-metrics' package.
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