Data Science Engineering Suite - Quick Reference
This skill turns raw data and questions into validated, documented models ready for production:
EDA workflows: Structured exploration with drift detection Feature engineering: Reproducible feature pipelines with leakage prevention and train/serve parity Model selection: Baselines first; strong tabular defaults; escalate complexity only when justified Evaluation & reporting: Slice analysis, uncertainty, model cards, production metrics SQL transformation: SQLMesh for staging/intermediate/marts layers MLOps: CI/CD, CT (continuous training), CM (continuous monitoring) Production patterns: Data contracts, lineage, feedback loops, streaming features
Modern emphasis (2026): Feature stores, automated retraining, drift monitoring (Evidently), train-serve parity, and agentic ML loops (plan -> execute -> evaluate -> improve). Tools: LightGBM, CatBoost, scikit-learn, PyTorch, Polars (lazy eval for larger-than-RAM datasets), lakeFS for data versioning.
Quick Reference Task Tool/Framework Command When to Use EDA & Profiling Pandas, Great Expectations df.describe(), ge.validate() Initial data exploration and quality checks Feature Engineering Pandas, Polars, Feature Stores df.transform(), Feast materialization Creating lag, rolling, categorical features Model Training Gradient boosting, linear models, scikit-learn lgb.train(), model.fit() Strong baselines for tabular ML Hyperparameter Tuning Optuna, Ray Tune optuna.create_study(), tune.run() Optimizing model parameters SQL Transformation SQLMesh sqlmesh plan, sqlmesh run Building staging/intermediate/marts layers Experiment Tracking MLflow, W&B mlflow.log_metric(), wandb.log() Versioning experiments and models Model Evaluation scikit-learn, custom metrics metrics.roc_auc_score(), slice analysis Validating model performance Data Lake & Lakehouse
For comprehensive data lake/lakehouse patterns (beyond SQLMesh transformation), see data-lake-platform:
Table formats: Apache Iceberg, Delta Lake, Apache Hudi Query engines: ClickHouse, DuckDB, Apache Doris, StarRocks Alternative transformation: dbt (alternative to SQLMesh) Ingestion: dlt, Airbyte (connectors) Streaming: Apache Kafka patterns Orchestration: Dagster, Airflow
This skill focuses on ML feature engineering and modeling. Use data-lake-platform for general-purpose data infrastructure.
Related Skills
For adjacent topics, reference:
ai-mlops - APIs, batch jobs, monitoring, drift, data ingestion (dlt) ai-llm - LLM prompting, fine-tuning, evaluation ai-rag - RAG pipelines, chunking, retrieval ai-llm-inference - LLM inference optimization, quantization ai-ml-timeseries - Time series forecasting, backtesting qa-testing-strategy - Test-driven development, coverage data-sql-optimization - SQL optimization, index patterns (complements SQLMesh) data-lake-platform - Data lake/lakehouse infrastructure (ClickHouse, Iceberg, Kafka) Decision Tree: Choosing Data Science Approach User needs ML for: [Problem Type] - Tabular data? - Small-medium (<1M rows)? -> LightGBM (fast, efficient) - Large and complex (>1M rows)? -> LightGBM first, then NN if needed - High-dim sparse (text, counts)? -> Linear models, then shallow NN
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Time series?
- Seasonality? -> LightGBM, then see ai-ml-timeseries
- Long-term dependencies? -> Transformers (see ai-ml-timeseries)
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Text or mixed modalities?
- LLMs/Transformers -> See ai-llm
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SQL transformations?
- SQLMesh (staging/intermediate/marts layers)
Rule of thumb: For tabular data, tree-based gradient boosting is a strong baseline, but must be validated against alternatives and constraints.
Core Concepts (Vendor-Agnostic) Problem framing: define success metrics, baselines, and decision thresholds before modeling. Leakage prevention: ensure all features are available at prediction time; split by time/group when appropriate. Uncertainty: report confidence intervals and stability (fold variance, bootstrap) rather than single-point metrics. Reproducibility: version code/data/features, fix seeds, and record the environment. Operational handoff: define monitoring, retraining triggers, and rollback criteria with MLOps. Implementation Practices (Tooling Examples) Track experiments and artifacts (run id, commit hash, data version). Add data validation gates in pipelines (schema + distribution + freshness). Prefer reproducible, testable feature code (shared transforms, point-in-time correctness). Use datasheets/model cards and eval reports as deployment prerequisites (Datasheets for Datasets: https://arxiv.org/abs/1803.09010; Model Cards: https://arxiv.org/abs/1810.03993). Do / Avoid
Do
Do start with baselines and a simple model to expose leakage and data issues early. Do run slice analysis and document failure modes before recommending deployment. Do keep an immutable eval set; refresh training data without contaminating evaluation.
Avoid
Avoid random splits for temporal or user-correlated data. Avoid "metric gaming" (optimizing the number without validating business impact). Avoid training on labels created after the prediction timestamp (silent future leakage). Core Patterns (Overview) Pattern 1: End-to-End DS Project Lifecycle
Use when: Starting or restructuring any DS/ML project.
Stages:
Problem framing - Business objective, success metrics, baseline Data & feasibility - Sources, coverage, granularity, label quality EDA & data quality - Schema, missingness, outliers, leakage checks Feature engineering - Per data type with feature store integration Modelling - Baselines first, then LightGBM, then complexity as needed Evaluation - Offline metrics, slice analysis, error analysis Reporting - Model evaluation report + model card MLOps - CI/CD, CT (continuous training), CM (continuous monitoring)
Detailed guide: EDA Best Practices
Pattern 2: Feature Engineering
Use when: Designing features before modelling or during model improvement.
By data type:
Numeric: Standardize, handle outliers, transform skew, scale Categorical: One-hot/ordinal (low cardinality), target/frequency/hashing (high cardinality) Feature Store Integration: Store encoders, mappings, statistics centrally Text: Cleaning, TF-IDF, embeddings, simple stats Time: Calendar features, recency, rolling/lag features
Key Modern Practice: Use feature stores (Feast, Tecton, Databricks) for versioning, sharing, and train-serve parity.
Detailed guide: Feature Engineering Patterns
Pattern 3: Data Contracts & Lineage
Use when: Building production ML systems with data quality requirements.
Components:
Contracts: Schema + ranges/nullability + freshness SLAs Lineage: Track source -> feature store -> train -> serve Feature store hygiene: Materialization cadence, backfill/replay, encoder versioning Schema evolution: Backward/forward-compatible migrations with shadow runs
Detailed guide: Data Contracts & Lineage
Pattern 4: Model Selection & Training
Use when: Picking model families and starting experiments.
Decision guide (modern benchmarks):
Tabular: Start with a strong baseline (linear/logistic, then gradient boosting) and iterate based on error analysis Baselines: Always implement simple baselines first (majority class, mean, naive forecast) Train/val/test splits: Time-based (forecasting), group-based (user/item leakage), or random (IID) Hyperparameter tuning: Start manual, then Bayesian optimization (Optuna, Ray Tune) Overfitting control: Regularization, early stopping, cross-validation
Detailed guide: Modelling Patterns
Pattern 5: Evaluation & Reporting
Use when: Finalizing a model candidate or handing over to production.
Key components:
Metric selection: Primary (ROC-AUC, PR-AUC, RMSE) + guardrails (calibration, fairness) Threshold selection: ROC/PR curves, cost-sensitive, F1 maximization Slice analysis: Performance by geography, user segments, product categories Error analysis: Collect high-error examples, cluster by error type, identify systematic failures Uncertainty: Confidence intervals (bootstrap where appropriate), variance across folds, and stability checks Evaluation report: 8-section report (objective, data, features, models, metrics, slices, risks, recommendation) Model card: Documentation for stakeholders (intended use, data, performance, ethics, operations)
Detailed guide: Evaluation Patterns
Pattern 6: Reproducibility & MLOps
Use when: Ensuring experiments are reproducible and production-ready.
Modern MLOps (CI/CD/CT/CM):
CI (Continuous Integration): Automated testing, data validation, code quality CD (Continuous Delivery): Environment-specific promotion (dev -> staging -> prod), canary deployment CT (Continuous Training): Drift-triggered and scheduled retraining CM (Continuous Monitoring): Real-time data drift, performance, system health
Versioning:
Code (git commit), data (DVC, LakeFS), features (feature store), models (MLflow Registry) Seeds (reproducibility), hyperparameters (experiment tracker)
Detailed guide: Reproducibility Checklist
Pattern 7: Feature Freshness & Streaming
Use when: Managing real-time features and streaming pipelines.
Components:
Freshness contracts: Define freshness SLAs per feature, monitor lag, alert on breaches Batch + stream parity: Same feature logic across batch/stream, idempotent upserts Schema evolution: Version schemas, add forward/backward-compatible parsers, backfill with rollback Data quality gates: PII/format checks, range checks, distribution drift (KL, KS, PSI)
Detailed guide: Feature Freshness & Streaming
Pattern 8: Production Feedback Loops
Use when: Capturing production signals and implementing continuous improvement.
Components:
Signal capture: Log predictions + user edits/acceptance/abandonment (scrub PII) Labeling: Route failures/edge cases to human review, create balanced sets Dataset refresh: Periodic refresh (weekly/monthly) with lineage, protect eval set Online eval: Shadow/canary new models, track solve rate, calibration, cost, latency
Detailed guide: Production Feedback Loops
Resources (Detailed Guides)
For comprehensive operational patterns and checklists, see:
EDA Best Practices - Structured workflow for exploratory data analysis Feature Engineering Patterns - Operational patterns by data type Data Contracts & Lineage - Data quality, versioning, feature store ops Modelling Patterns - Model selection, hyperparameter tuning, train/test splits Evaluation Patterns - Metrics, slice analysis, evaluation reports, model cards Reproducibility Checklist - Experiment tracking, MLOps (CI/CD/CT/CM) Feature Freshness & Streaming - Real-time features, schema evolution Production Feedback Loops - Online learning, labeling, canary deployment Templates
Use these as copy-paste starting points:
Project & Workflow Templates Standard DS project template: assets/project/template-standard.md Quick DS experiment template: assets/project/template-quick.md Feature Engineering & EDA Feature engineering template: assets/features/template-feature-engineering.md EDA checklist & notebook template: assets/eda/template-eda.md Evaluation & Reporting Model evaluation report: assets/evaluation/template-evaluation-report.md Model card: assets/evaluation/template-model-card.md ML experiment review: assets/review/experiment-review-template.md SQL Transformation (SQLMesh)
For SQL-based data transformation and feature engineering:
SQLMesh project setup: ../data-lake-platform/assets/transformation/sqlmesh/template-sqlmesh-project.md SQLMesh model types: ../data-lake-platform/assets/transformation/sqlmesh/template-sqlmesh-model.md (FULL, INCREMENTAL, VIEW) Incremental models: ../data-lake-platform/assets/transformation/sqlmesh/template-sqlmesh-incremental.md DAG and dependencies: ../data-lake-platform/assets/transformation/sqlmesh/template-sqlmesh-dag.md Testing and data quality: ../data-lake-platform/assets/transformation/sqlmesh/template-sqlmesh-testing.md
Use SQLMesh when:
Building SQL-based feature pipelines Managing incremental data transformations Creating staging/intermediate/marts layers Testing SQL logic with unit tests and audits
For data ingestion (loading raw data), use:
ai-mlops skill (dlt templates for REST APIs, databases, warehouses) Navigation
Resources
references/reproducibility-checklist.md references/evaluation-patterns.md references/feature-engineering-patterns.md references/modelling-patterns.md references/feature-freshness-streaming.md references/eda-best-practices.md references/data-contracts-lineage.md references/production-feedback-loops.md
Templates
assets/project/template-standard.md assets/project/template-quick.md assets/features/template-feature-engineering.md assets/eda/template-eda.md assets/evaluation/template-evaluation-report.md assets/evaluation/template-model-card.md assets/review/experiment-review-template.md template-sqlmesh-project.md template-sqlmesh-model.md template-sqlmesh-incremental.md template-sqlmesh-dag.md template-sqlmesh-testing.md
Data
data/sources.json - Curated external references External Resources
See data/sources.json for curated foundational and implementation references:
Core ML/DL: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, JAX Data processing: pandas, NumPy, Polars, DuckDB, Spark, Dask SQL transformation: SQLMesh, dbt (staging/marts/incremental patterns) Feature stores: Feast, Tecton, Databricks Feature Store (centralized feature management) Data validation: Pydantic, Great Expectations, Pandera, Evidently (quality + drift) Visualization: Matplotlib, Seaborn, Plotly, Streamlit, Dash MLOps: MLflow, W&B, DVC, Neptune (experiment tracking + model registry) Hyperparameter tuning: Optuna, Ray Tune, Hyperopt Model serving: BentoML, FastAPI, TorchServe, Seldon, Ray Serve Orchestration: Kubeflow, Metaflow, Prefect, Airflow, ZenML Cloud platforms: AWS SageMaker, Google Vertex AI, Azure ML, Databricks, Snowflake
Use this skill to execute data science projects end-to-end: concrete checklists, patterns, and templates, not theory.