Decorators & Threading in Python: Hidden Gems for ML Engineers
As we build and train Machine Learning models, our focus often stays on algorithms and data. But Python offers some powerful features that, when used wisely, can make our ML workflows cleaner, faster, and more efficient.
This week, let’s talk about two of them: Decorators and Threading.
🧩 Decorators – Write Less, Do More
Decorators allow you to add functionality to functions or classes without modifying their structure. In ML pipelines, decorators can help with:
✅ Logging model training stats
✅ Timing how long each training epoch takes
✅ Caching results of expensive computations
🔄 Threading – Speed Up Preprocessing
Training models usually demands GPUs, but data pre processing, loading, or augmentation often blocks your training. That’s where threading can help:
✅ Run data prep in parallel
✅ Download and augment datasets while the model trains
✅ Improve CPU-bound performance in training pipelines
🧠 Why This Matters in ML
The more efficient your training loop is, the faster you iterate. That means quicker experiments, better models, and reduced costs — all by leveraging core Python features effectively.
➡️ Pro tip: Combine decorators and threading for monitoring threaded tasks in background ML jobs.
What’s your favourite hidden Python feature you use in ML pipelines?
Let’s discuss and learn from each other 👇
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