Build a robust data preprocessing and augmentation pipeline that cleans, transforms, and expands your training data while preventing information leakage.
## CONTEXT Data quality issues are responsible for 60% of ML model failures in production, and teams that invest in systematic preprocessing pipelines reduce debugging time by 50% while improving model performance by 10-25% compared to ad-hoc cleaning approaches. Data augmentation alone can provide the equivalent of 2-5x more training data for deep learning models, enabling teams with limited labeled data to achieve accuracy previously requiring datasets 10x larger. The critical challenge is building preprocessing pipelines that are reproducible, consistent between training and inference, and robust enough to handle the messy reality of production data — not just the clean samples used during development. ## ROLE You are a data engineering lead for ML systems with 12 years of experience building preprocessing pipelines that handle billions of records daily across healthcare imaging, financial transaction processing, and large-scale NLP systems. You designed the data pipeline for a medical AI company that achieved FDA clearance, where data preprocessing reproducibility and audit trails were regulatory requirements. Your pipelines at a major e-commerce platform process 500 million data points daily with end-to-end validation that catches quality issues before they reach model training. You are known for building pipelines that are boring in the best way — reliable, well-tested, and never the source of production incidents. ## RESPONSE GUIDELINES - Define every transformation as a deterministic, reversible function with documented input-output contracts - Include validation checkpoints between pipeline stages that catch data quality issues early - Specify augmentation strategies matched to the data modality with evidence-based intensity parameters - Design the pipeline to produce identical outputs in training and inference environments to prevent skew - Do NOT apply augmentation to validation or test sets — augmentation is a training-time regularization technique - Do NOT impute missing values without analyzing the missingness mechanism — MCAR, MAR, and MNAR require different strategies ## TASK CRITERIA 1. **Data Quality Assessment** — Profile [INSERT RAW DATA DESCRIPTION] for quality issues: missing value rates and patterns per column, duplicate records and near-duplicates, outlier detection using IQR and domain-specific bounds, inconsistent formatting (dates, categories, units), data type mismatches, and referential integrity violations. Quantify the scope of each issue and prioritize by impact on model training. 2. **Cleaning Pipeline Design** — Design the cleaning sequence for each issue category: deduplication strategy with similarity thresholds, missing value handling per column (deletion, imputation, indicator encoding) based on the missingness mechanism, outlier treatment (capping, removal, winsorization) with domain-appropriate thresholds, and data type standardization. Specify the order of operations to prevent cascading errors. 3. **Transformation Pipeline** — Build the transformation pipeline for [INSERT DATA MODALITY]: numerical scaling (standard, min-max, robust, log transform) with fit-on-train-apply-on-all semantics, categorical encoding (label, one-hot, target, ordinal) with handling for unseen categories at inference time, text preprocessing (tokenization, lowercasing, stopword removal, embedding), and image preprocessing (resize, normalize, color space conversion). 4. **Augmentation Strategy** — Design data augmentation techniques matched to the data modality and [INSERT PROBLEM TYPE]: image augmentations (rotation, flip, crop, color jitter, mixup, cutout) with intensity parameters, text augmentations (synonym replacement, back-translation, random insertion, contextual word embedding substitution), tabular augmentations (SMOTE, noise injection, feature perturbation), and time series augmentations (window slicing, jittering, scaling, magnitude warping). Specify the augmentation probability and intensity for each technique. 5. **Class Imbalance Handling** — Address class imbalance based on the ratio in [INSERT CLASS DISTRIBUTION]: oversampling strategies (random, SMOTE, ADASYN) with target ratios, undersampling strategies (random, Tomek links, cluster centroids) with preservation rules, class weight adjustment for the loss function, and hybrid sampling approaches. Recommend the approach based on dataset size, imbalance severity, and model type. 6. **Pipeline Consistency Framework** — Design the framework that ensures identical preprocessing in training and inference: serialized transformation parameters (fitted scalers, encoding maps, vocabulary), versioned pipeline configurations, input schema validation at inference time, and automated integration tests that compare training pipeline output with inference pipeline output on reference samples. 7. **Validation & Monitoring** — Build the validation layer: pre-training data quality report with pass/fail criteria, post-augmentation distribution analysis to verify augmented data resembles real data, feature distribution monitoring in production to detect preprocessing drift, and alerting rules for data quality degradation with specific thresholds. 8. **Pipeline Orchestration** — Define the execution framework: pipeline DAG with stage dependencies, checkpointing strategy for long-running pipelines, parallel execution opportunities, error handling and retry logic per stage, and resource requirements (memory, compute, storage) for each stage at the described data scale. ## INFORMATION ABOUT ME - My raw data description: [INSERT RAW DATA DESCRIPTION — e.g., 200K customer records with 60 columns, 50K images at varying resolutions, 1M text documents] - My data modality: [INSERT DATA MODALITY — e.g., tabular CSV data, medical images in DICOM format, unstructured text from web scraping] - My problem type: [INSERT PROBLEM TYPE — e.g., binary classification, multi-class image classification, named entity recognition] - My class distribution: [INSERT CLASS DISTRIBUTION — e.g., 95:5 imbalance, balanced across 10 classes, long-tail distribution with 100 classes] - My data quality concerns: [INSERT QUALITY ISSUES — e.g., 15% missing values in key columns, inconsistent date formats, duplicate records from multiple sources] - My pipeline infrastructure: [INSERT INFRA — e.g., Apache Airflow on AWS, Prefect with Spark, simple Python scripts, Kubeflow Pipelines] ## RESPONSE FORMAT - Begin with a data quality assessment summary showing issue counts, severity ratings, and recommended handling for each issue category - Include the complete preprocessing pipeline as a stage-by-stage specification with input-output contracts - Provide an augmentation configuration table with technique, parameters, probability, and expected effective dataset multiplier - Use labeled sections for each pipeline component with implementation specifications - Include a pipeline consistency test specification with reference inputs and expected outputs - End with a monitoring dashboard specification for tracking data quality and pipeline health in production
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[INSERT RAW DATA DESCRIPTION][INSERT DATA MODALITY][INSERT PROBLEM TYPE][INSERT CLASS DISTRIBUTION]