A lot of computer vision performance comes from cleaner inputs rather than larger models. I use OpenCV for resizing, denoising, thresholding, and contour extraction when preparing images for OCR or downstream classification. These classical steps ofte

Location data becomes useful when spatial joins and distance-based features are handled correctly. GeoPandas is enough for many routing, service coverage, and market analysis tasks before you need heavier GIS infrastructure. I care about coordinate sy

Regex is not glamorous, but it remains one of the fastest ways to turn messy text into useful structured fields. I use it for IDs, dates, codes, and log fragments before reaching for heavier NLP. The important part is making patterns specific enough t

For lightweight data collection, I prefer reliable HTML parsing over brittle browser automation. That means stable headers, polite rate limiting, retries, and explicit extraction rules. If scraping becomes core infrastructure, then I graduate it into

A surprising amount of feature engineering is best done in SQL before Python ever runs. ROW_NUMBER, LAG, rolling windows, and partitioned aggregates are ideal for deriving customer behavior signals close to the source. I use SQL here when it reduces m

Before retraining, I want hard guarantees that the data feed still looks structurally sane. Great Expectations gives teams a shared validation language that analysts, ML engineers, and data engineers can all inspect. I use it to codify invariants that

I use schema validation to stop bad data before it poisons training or inference. Pandera lets me express expectations around types, nullability, ranges, and uniqueness in code that can run in CI or orchestration jobs. This catches upstream breakage e

If experiments matter, they should be searchable after the notebook is closed. MLflow gives me parameter tracking, metric history, artifact storage, and a lightweight model registry without much ceremony. It is one of the fastest ways to make a small

Model serialization is not just a file-format choice. It affects startup time, compatibility, portability, and security boundaries. I use joblib for common scikit-learn pipelines, reserve pickle for trusted internal workflows, and reach for ONNX when

Deployment should not rewrite the feature logic from scratch. I expose trained pipelines behind FastAPI so the exact preprocessing and estimator objects travel together. Strong request schemas and explicit model versioning keep this boring in the righ

Anomaly detection is mostly about defining normal behavior well enough that deviations matter. I usually combine a model like IsolationForest with feature windows and operational thresholds that the business can interpret. Without that calibration, an

For many business forecasting tasks, a carefully tuned statistical baseline is still the right first step. SARIMAX makes seasonality, trend, and external regressors explicit, which is useful when stakeholders want understandable drivers. I use it befo