azure-ai-ml

v1.33.0 safe
4.0
Medium Risk

Microsoft Azure Machine Learning Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package shows some potential risks related to shell usage and incomplete metadata, but these do not strongly suggest malicious intent or a supply-chain attack. Overall, the package appears safe.

  • Use of 'shell=True' which requires careful handling.
  • Incomplete author information and a single-package maintainer.
Per-check LLM notes
  • Network: No network calls detected, which is normal for many packages.
  • Shell: The use of 'shell=True' can be risky if not handled properly, but it may be necessary for interacting with Azure CLI commands within the package.
  • Obfuscation: The observed pattern is commonly used for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The author information is incomplete and the maintainer has a single package, which could indicate a less established or potentially suspicious account.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (39872 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

  • 176 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • ------------------ __path__ = __import__("pkgutil").extend_path(__path__, __name__) # ------------------------
  • ----------------- __path__ = __import__("pkgutil").extend_path(__path__, __name__) import logging from typing
  • ----------------- __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore from .arm
  • ----------------- __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # --------
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • ] = timeout output = subprocess.check_output(command_to_execute, **subprocess_args).decode(encoding="UTF-
  • construct a command because "shell=True" flag, used below, doesn't work with the vector # argv
  • ed or not. # We need "shell=True" flag so that the "az" wrapper works. # We also pa
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: microsoft.com> license-expression: mit

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python 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 azure-ai-ml 
Your task is to develop a mini-application using the 'azure-ai-ml' Python package that simplifies the process of deploying machine learning models on Azure. This application will serve as a user-friendly interface for uploading datasets, training models, and deploying them as web services. Here are the steps and features your application should include:

1. **Setup and Configuration**: Begin by setting up your environment with the necessary packages, including 'azure-ai-ml'. Ensure you have an Azure account and the appropriate Azure ML workspace setup.
2. **User Interface**: Design a simple command-line interface (CLI) or a basic web interface where users can interact with the application. The CLI/web interface should allow users to upload their dataset files (CSV, Excel, etc.), select algorithms for model training, and deploy trained models.
3. **Data Handling**: Implement functionality to handle various types of datasets. Users should be able to upload datasets and preprocess them if needed (e.g., cleaning data, handling missing values).
4. **Model Training**: Use the 'azure-ai-ml' package to train models using selected algorithms. Allow users to choose from a variety of algorithms such as Linear Regression, Decision Trees, Random Forests, SVM, etc. The application should also provide options to tune hyperparameters.
5. **Model Evaluation**: After training, evaluate the models based on common metrics relevant to the type of problem (classification/regression). Provide visualizations of evaluation results if possible.
6. **Deployment**: Once a satisfactory model is chosen, use the 'azure-ai-ml' package to deploy it as a web service. The application should guide users through the deployment process, allowing them to configure endpoints and monitor deployment status.
7. **Prediction Service**: After deployment, the application should offer a way to test predictions using the deployed model. Users can input new data and receive predictions directly from the application.
8. **Documentation and Support**: Include clear documentation on how to use the application, including setup instructions, API documentation, and troubleshooting tips.

By completing this project, you'll gain hands-on experience with the 'azure-ai-ml' package and understand how to leverage Azure's capabilities for machine learning tasks.

💬 Discussion Feed

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