| 🤝 COLLABORATION |
Learn ML Engineering for Free on ML Zoomcamp
Learn ML engineering for free on ML Zoomcamp and receive a certificate! Join online for practical, hands-on experience with the tech stack and workflows used in production ML. The next cohort of the course starts on September 15, 2025. Here’s what you’ll learn:
Core foundations:
- Python ecosystem: Jupyter, NumPy, Pandas, Matplotlib, Seaborn
- ML frameworks: Scikit-learn, TensorFlow, Keras
Applied projects:
- Supervised learning with CRISP-DM framework
- Classification/regression with evaluation metrics
- Advanced models: decision trees, ensembles, neural nets, CNNs
Production deployment:
- APIs and containers: Flask, Docker, Kubernetes
- Cloud solutions: AWS Lambda, TensorFlow Serving/Lite
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| 📅 Today’s Picks |
Ruff: Stop AI Code Complexity Before It Hits Production
Problem:
AI agents often create overengineered code with multiple nested if/else and try/except blocks, increasing technical debt and making functions difficult to test.
However, it is time-consuming to check each function manually.
Solution:
Ruff’s C901 complexity check automatically flags overly complex functions before they enter your codebase.
This tool counts decision points (if/else, loops) that create multiple execution paths in your code.
Key benefits:
- Automatic detection of complex functions during development
- Configurable complexity thresholds for your team standards
- Integration with pre-commit hooks for automated validation
- Clear error messages showing exact complexity scores
No more manual code reviews to catch overengineered functions.
Full Article:
Build Debuggable Tests: One Assertion Per Function
Problem:
Tests with multiple assertions make debugging harder.
When a test fails, you can’t tell which assertion broke without examining the code.
Solution:
Create multiple specific test functions for different scenarios of the same function.
Follow these practices for focused test functions:
- One assertion per test function for clear failure points
- Use descriptive test names that explain the expected behavior
- Maintain consistent naming patterns across your test suite
This approach makes your test suite more maintainable and failures easier to diagnose.
Full Article:
