autoguard-ml

v0.2.3 suspicious
4.0
Medium Risk

Unified AutoML + Dataset Diagnosis + Drift Detection for production ML

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network usage, shell execution, obfuscation, and credential handling. However, the missing maintainer's author name and the potential inactivity of the account increase suspicion.

  • Missing maintainer's author name
  • Potential inactivity of the maintainer's account
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: Shell execution seems to be for internal operations like running system commands or checking GPU status, which is typical for ML packages.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author name is missing and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://autoguard-ml.readthedocs.io
  • Detailed PyPI description (27325 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 108 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 20 commits in Nikhil-Parab/autoguard-ml
  • Single author but highly active (20 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • ed) try: result = subprocess.run( [ sys.executable, "-c",
  • ort try: result = subprocess.run( ["nvidia-smi", "--query-gpu=name", "--format=cs
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Nikhil-Parab/autoguard-ml appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 autoguard-ml 
Create a mini-application that leverages the 'autoguard-ml' package to streamline the deployment of machine learning models in a production environment. Your task is to develop a tool that not only automates the model training process but also continuously monitors the dataset for any drifts and diagnoses potential issues within the data. Here's a detailed breakdown of the steps and features your application should include:

1. **Setup and Configuration**: Begin by setting up a basic Python environment where you install the 'autoguard-ml' package along with other necessary dependencies like pandas, numpy, and scikit-learn. Define a configuration file where users can specify their datasets, target variables, and model types.
2. **Data Import and Preprocessing**: Develop a feature that allows users to upload their datasets. Use 'autoguard-ml' to automatically preprocess the data, including handling missing values, encoding categorical variables, and scaling numerical features.
3. **Model Training and Selection**: Implement functionality that uses 'autoguard-ml' to train multiple models from a specified list (e.g., Random Forest, Gradient Boosting, Neural Networks). This feature should automatically select the best performing model based on predefined metrics.
4. **Dataset Diagnosis**: Utilize 'autoguard-ml' to diagnose the uploaded datasets for common issues such as class imbalance, outlier detection, and multicollinearity. Provide a summary report detailing any identified problems and suggestions for improvements.
5. **Drift Detection**: Incorporate real-time monitoring using 'autoguard-ml' to detect if there is any concept drift in the incoming data after the model has been deployed. Ensure that the application alerts the user if significant changes are detected.
6. **User Interface**: Design a simple web-based UI where users can interact with all the above functionalities. The UI should allow uploading files, viewing preprocessing reports, selecting models, and receiving alerts about drift detection.
7. **Documentation and Deployment**: Finally, write comprehensive documentation explaining how to use the application and deploy it in a cloud environment (such as AWS or Google Cloud).

This project aims to demonstrate the power of 'autoguard-ml' in simplifying the complexities involved in deploying robust machine learning solutions.

💬 Discussion Feed

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