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name: ai-ml-data-science description: "End-to-end data science patterns (modern best practices): problem framing -> data -> EDA -> feature engineering (with feature stores) -> modelling -> evaluation -> reporting, plus SQL transformation (SQLMesh). Emphasizes MLOps integration, drift monitoring, and production-ready workflows."

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 (January 2026): Feature stores, automated retraining, drift monitoring (Evidently AI), train-serve parity, and agentic ML loops (plan → execute → evaluate → improve). Tools: LightGBM 4.6, CatBoost, scikit-learn 1.7, PyTorch 2.9.1, Polars 1.x (lazy eval for >RAM datasets), lakeFS for data versioning.

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Quick Reference

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When to Use This Skill

Claude should invoke this skill when the user asks for hands-on DS/ML workflow help, e.g.:

• "Help me explore this dataset / find issues."
• "Design features for this problem."
• "Choose metrics and evaluate this model."
• "Write a model evaluation report or model card."
• "Structure an end-to-end DS project."
• "Set up SQL transformations with SQLMesh."
• "Build incremental feature pipelines in SQL."
• "Create staging/intermediate/marts layers."
• "Set up MLOps: CI/CD, continuous training, monitoring."

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.

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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)

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Decision Tree: Choosing Data Science Approach

CopyUser needs ML for: [Problem Type]
    ├─ Tabular Data?
    │   ├─ Small-Medium (<1M rows)? → **LightGBM** (fast, efficient)
    │   ├─ Large & Complex (>1M rows)? → **LightGBM** first, then NN if needed
    │   └─ High-dim sparse (text, counts)? → Linear models, then shallow NN
    │
    ├─ Time Series?
    │   ├─ Seasonality? → LightGBM, then see ai-ml-timeseries skill
    │   └─ Long-term dependencies? → Transformers (ai-ml-timeseries)
    │
    ├─ Text or Mixed Modalities?
    │   └─ LLMs/Transformers → See ai-llm
    │
    └─ 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.

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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:

1. Problem framing - Business objective, success metrics, baseline
2. Data & feasibility - Sources, coverage, granularity, label quality
3. EDA & data quality - Schema, missingness, outliers, leakage checks
4. Feature engineering - Per data type with feature store integration
5. Modelling - Baselines first, then LightGBM, then complexity as needed
6. Evaluation - Offline metrics, slice analysis, error analysis
7. Reporting - Model evaluation report + model card
8. MLOps - CI/CD, CT (continuous training), CM (continuous monitoring)

Detailed guide: EDA Best Practices

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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

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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

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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

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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

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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

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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

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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

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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

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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: assets/transformation/template-sqlmesh-project.md
• SQLMesh model types: assets/transformation/template-sqlmesh-model.md (FULL, INCREMENTAL, VIEW)
• Incremental models: assets/transformation/template-sqlmesh-incremental.md
• DAG and dependencies: assets/transformation/template-sqlmesh-dag.md
• Testing and data quality: assets/transformation/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

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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.