Event time vs arrival time
| Data | Event time | Arrives | Usable at a 02:00 batch score |
|---|---|---|---|
| App events (streamed) | t | t + seconds | up to 01:59 |
| Daily warehouse load | t | next day 06:00 | up to the day before yesterday |
| Support tickets (CRM export) | t | t + 24 h | up to yesterday 02:00 |
| Chargebacks | t | t + 30-120 days | only old transactions |
Model training must use the same availability as production. If the table has an ingested_at column,
filter on it (ingested_at < anchor); otherwise set --latency to the typical delay (or a conservative
upper bound).
Late-arriving and corrected data
- Events can arrive late (offline devices, batch partners). Production at time a did not see them; training that includes them is optimistic. Prefer arrival-time filtering.
- Records can be corrected later (refunds amend amounts, status changes). Use an append-only event log, or snapshot feature values as computed at the time.
Backfills
When adding a new feature, historical values must be computed as they would have been at each past anchor:
- Use the event log with arrival times.
- Recompute per anchor with the same code that runs in production (or the feature store's point-in-time join).
- Validate: for recent dates where production already logged the feature, compare backfilled vs logged values. Differences reveal latency or logic mismatches.
Online/offline parity
| Concern | Practice |
|---|---|
| Same definitions | one feature definition (SQL / feature store) for batch training and online serving |
| Same windows | online stream aggregations with the same window lengths and closedness |
| Same anchor | online: events up to "now" minus processing delay; offline: same latency |
| Missing history | online new entities get the same defaults (0 counts, NaN means) as offline |
| Logging | log the features actually served; train future models on logged values when possible |
Streaming windows
Real-time velocity features are maintained by a stream processor (Flink, Kafka Streams, Spark Structured Streaming) or an online feature store with sliding/hopping windows. Notes:
- Sliding windows of exact length are expensive; hopping windows (e.g. 24h window, 5-minute hop) approximate them. Train on the same approximation.
- Watermarks control late data: events later than the watermark are dropped online; drop them offline too.
- Exclude the current event explicitly when the feature is computed after it is ingested.
anomaly-detection-model-builder
Build and evaluate anomaly detection for tabular, sensor and transaction data with few or no labels: define the anomaly type (point, contextual, collective), choose detectors (robust z-score/MAD and IQR rules, Mahalanobis with robust covariance, k-NN distance, LOF, Isolation Forest, one-class SVM, PCA reconstruction / autoencoders, forecasting residuals for time series), combine them by rank, explain each alert by its deviating features, set thresholds from an alert budget or the score distribution, and evaluate with precision@k, PR-AUC and recall on known incidents, or detector agreement when no labels exist. Includes pure-Python detectors (robust z, Mahalanobis, kNN, Isolation Forest, ensemble) and an evaluation script.
catboost-model-builder
Build CatBoost models that use its strengths correctly: raw categorical columns as cat_features (ordered target statistics, no manual encoding), text_features, Pool construction with NaN-safe categories, ordered boosting and has_time for temporal data, symmetric (oblivious) trees, overfitting detector with eval_set and use_best_model, losses for classification, regression (Quantile, Poisson, Tweedie) and ranking (YetiRank), class weights, monotone constraints, feature importance (PredictionValuesChange, LossFunctionChange) and SHAP values, GPU training and model export. Plans cat/text/numeric columns from a CSV and generates a runnable training script, and checks parameter sets for the conflicts CatBoost rejects (iterations vs n_estimators, Bayesian bootstrap with subsample, multiple class-weight options, depth > 16, use_best_model without eval_set).
categorical-encoding-selector
Choose and implement the right encoding for every categorical column given the model family and the data: one-hot (with infrequent-level grouping and handle_unknown), ordinal with an explicit order, cross-fitted target encoding (scikit-learn TargetEncoder, CatBoost ordered statistics, category_encoders inside CV), frequency/count encoding, feature hashing, native categorical support in LightGBM, HistGradientBoosting, XGBoost and CatBoost, prefix levels for codes (zip3, NAICS-2), and embeddings. Measures cardinality, rare-level share, unseen-level rate on a time-ordered holdout and target signal per column, emits the ColumnTransformer, and quantifies target-encoding leakage (in-sample vs out-of-fold vs holdout AUC).
classification-model-selector
Choose the right classification algorithm for tabular, text or mixed data and prove the choice with a fair bake-off: logistic regression (L2, L1, elastic net), linear SVM, naive Bayes, k-NN, RBF SVM, decision trees, random forest, extra trees, HistGradientBoosting, LightGBM, XGBoost, CatBoost, explainable boosting machines (EBM/GAM), points scorecards and MLPs, matched to data size, width, categorical and high-cardinality share, missingness, class imbalance, text, and constraints such as interpretability, monotonic constraints, calibrated probabilities, latency budgets and tiny deployable models. Ranks candidates with starting hyperparameters and cautions, then generates a bake-off script running every candidate on identical CV folds with per-fold metrics, fit time and prediction latency.
clustering-algorithm-selector
Choose the right clustering algorithm and check that clustering makes sense at all: cluster tendency (Hopkins statistic with a correlated-features caveat, nearest-neighbour isolation), then k-means / MiniBatchKMeans, Gaussian mixtures with BIC, agglomerative (Ward, average, complete, single) with dendrograms, DBSCAN, HDBSCAN, OPTICS, spectral clustering, BIRCH, mean shift, k-prototypes and k-modes for mixed or categorical data, and Gower distance, matched to data size, expected shapes, noise, varying density, known k, soft memberships, hierarchy and streaming. Covers preprocessing (scaling, encoding, dimensionality, distance metrics), validation (internal indices, bootstrap stability, external labels, business profiling) and assigning new points.
collaborative-filtering-implementer
Implement collaborative filtering recommenders that beat popularity: item-based kNN with cosine, Jaccard or asymmetric similarity, shrinkage, neighbourhood size, positive-only or mean-centred rating signals and 'because you liked X' explanations; implicit ALS with confidence weighting, BPR pairwise ranking and the closed-form EASE model in plain NumPy, tuned on a temporal validation split by recall@k and NDCG@k and refit on all data; plus user-kNN, explicit matrix factorisation, the implicit/LightFM libraries, hyperparameter ranges, cold-start fallbacks, scaling with sparse matrices and approximate nearest neighbours. Produces top-k lists in the format of the recommendation evaluator.