Data Contracts in Data Engineering: Why They Matter
A source system changes a field, and pipelines downstream break silently. Here’s what data contracts in data engineering actually cover, and how automated tests relate.
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Notes on data engineering, AI and building things end to end.
Reproducible Python Environments for Machine Learning
“Works on my machine” usually traces back to an unpinned dependency. Here’s why a reproducible python environment for machine learning matters, and how to actually build one.
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SQL vs Python for Data Transformation: How to Choose
Both can transform data. Here’s how to actually choose between SQL and Python for data transformation, with real project examples of each done right.
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Precision at K vs Recall at K for Recommender Systems
A recommender returns a ranked list, not a single prediction. Here’s how precision at K and recall at K evaluate that differently from standard metrics, with a real project example.
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Learning Curves in Machine Learning: How to Read Them
“Should I collect more data, or is my model the problem?” Here’s how to read a learning curve in machine learning to actually answer that, with real project examples.
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Model Calibration in Machine Learning: Why It Matters
A 0.9 probability should mean right 90% of the time. Here’s what model calibration in machine learning actually checks, how to fix it, and a real project example.
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Batch vs Real-Time Inference: How to Choose
The same model can often serve either pattern. Here’s how to choose between batch vs real-time inference, with real project examples of each.
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Categorical Encoding Methods: One-Hot vs Label vs Target
A model can’t read text labels directly. Here’s how one-hot, label, and target encoding compare as categorical encoding methods, and which fits your model type.
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Outlier Detection Methods: IQR vs Z-Score vs Isolation Forest
Not every unusual value is a mistake. Here’s how IQR, Z-score, and Isolation Forest differ as outlier detection methods, and how to avoid deleting real signal by accident.
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Multicollinearity in Regression: How to Detect and Fix It
Correlated features can make regression coefficients unstable, even when accuracy looks fine. Here’s how to detect multicollinearity in regression and when it’s actually a problem worth fixing.
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