arize-ax-airflow-provider

v1.4.0 safe
3.0
Low Risk

Airflow provider for Arize AX: operators and hooks for datasets, experiments, projects, spans, and ML.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all assessed categories, with only minor red flags noted in metadata. There is no indication of malicious intent or supply-chain attack.

  • Low network, shell, obfuscation, and credential risks.
  • Minor red flags in metadata but no strong indicators of malice.
Per-check LLM notes
  • Network: Network calls are likely used for legitimate purposes such as API interactions or fetching data.
  • Shell: No shell execution patterns detected, indicating low risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some minor red flags, but no strong indicators of malice or supply-chain attack.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: pyproject.toml
  • 7 test file(s) detected (e.g. test_e2e_dag_callables.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://arize.com/docs/ax/integrations/orchestration/airflow
  • Detailed PyPI description (17954 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

  • 412 type-annotated function signatures detected in source
○ 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 score 3.0

Found 2 network call pattern(s)

  • ext_cursor resp = requests.get(base_url, params=params, headers=headers, timeout=30)
  • = name_search resp = requests.get(url, params=params, headers=headers, timeout=30) if
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://host.docker.internal:9000
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Arize AX Airflow Provider" 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 arize-ax-airflow-provider 
Create a mini-application named 'ML Experiment Tracker' using the 'arize-ax-airflow-provider' Python package. This application will serve as a bridge between Apache Airflow and Arize AX, allowing users to automate the tracking of machine learning experiments within their data pipelines. The application should include the following functionalities:

1. **Experiment Tracking**: Users should be able to create new ML experiments within their Airflow workflows, specifying details such as experiment name, dataset ID, and model version.
2. **Dataset Management**: Integrate the ability to manage datasets associated with experiments. This includes uploading datasets, tagging them with metadata, and linking them to specific experiments.
3. **Model Performance Monitoring**: Implement functionality to monitor the performance of different models over time. This involves logging metrics like accuracy, precision, recall, and F1 score at various stages of the experiment lifecycle.
4. **Visualization Dashboard**: Develop a simple dashboard that visualizes key performance indicators (KPIs) of the experiments, making it easier for stakeholders to understand the progress and outcomes.
5. **Alert System**: Set up an alert system that notifies users via email or Slack when certain thresholds are breached in terms of model performance or experiment status.
6. **Custom Hooks and Operators**: Utilize the 'arize-ax-airflow-provider' package to create custom hooks and operators tailored to the specific needs of ML experiment tracking. For example, a custom operator to automatically tag datasets based on predefined criteria, or a hook to fetch real-time performance metrics from Arize AX.

The application should be designed to be user-friendly and scalable, with clear documentation on how to integrate it into existing Airflow environments. Additionally, provide examples and best practices for utilizing the 'arize-ax-airflow-provider' package effectively in a production setting.

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