Data Processing & Reporting Checklist for AI & Machine Learning

Lock encoding, delimiter, quoting, timezones, and decimal separators, then validate with pyarrow.csv or pandas dtype maps to prevent silent coercion. Document column semantics and units so downstream ML features remain stable across runs.

Write to Parquet with Arrow types, Snappy or Zstandard compression, and statistics enabled for predicate pushdown. Partition by event_date or customer_id to speed up training set materialization in Spark, DuckDB, or BigQuery.

Use watermarks, change data capture, or file checkpoints so re-runs do not duplicate records. De-duplicate by composite keys and record hash, and persist ingestion logs for auditability.

Validate payloads with Pydantic, JSON Schema, or Marshmallow and reject non-conforming inputs early. Version the contract in Git, publish to producers, and monitor breaking changes with CI checks.

Use Polars or DuckDB for single-node columnar speed, switch to Spark or Dask when data no longer fits memory or requires distributed joins. Benchmark on realistic samples and set memory budgets to avoid OOM failures.

Create Great Expectations or Soda checks for null ratios, ranges, uniqueness, referential integrity, and regex patterns. Store suites in the repo and run them in CI and the orchestrator before every model refresh.

Generate YData Profiling summaries and use Evidently to compare training vs scoring distributions for top features and targets. Alert on population stability index thresholds to catch upstream shifts that degrade models.

Scan inputs with Microsoft Presidio or custom regex-plus-entropy detectors, then mask or tokenize sensitive fields. Keep a data minimization policy and audit logs to meet compliance requirements.

Emit OpenLineage events from Airflow, Dagster, or Prefect so Marquez or OpenMetadata can render flow graphs. Link datasets, jobs, and versions to quickly trace defects back to the source.

Define expected arrival windows, row counts, and anomaly thresholds, then route alerts to Slack or PagerDuty. Tag outages with incident cause codes to improve future prevention.

Normalize case, whitespace, and diacritics, then use recordlinkage or rapidfuzz for fuzzy joins when keys are dirty. Persist golden IDs and survivorship rules to stabilize feature sets.

For geocoding or firmographic APIs, add a local key-value cache with TTL and exponential backoff to avoid throttling. Log provider latency and fill-rate to ensure SLA compliance.

Define features in Feast with event timestamps and apply offline-to-online consistency checks. Prevent leakage by materializing training data via time-aware joins and backfills.

Create Sentence Transformers or CLIP embeddings and store vectors with metadata in Parquet or a vector DB like Faiss or Pinecone. Record the model name and hash for reproducibility across experiments.

Write transforms with Polars expressions, DuckDB SQL, or Spark SQL to leverage columnar execution. Avoid Python UDFs unless necessary, and unit test logic with small fixture datasets.

Use pdfplumber, Tabula, or Camelot for table-heavy PDFs, GROBID for scholarly articles, and Apache Tika or unstructured.io for mixed content. Benchmark accuracy on a labeled sample before committing.

Apply Unicode normalization, fix hyphenation, and perform sentence segmentation, but keep page numbers and coordinate spans for citations. Store provenance alongside text to enable traceable LLM outputs.

Resolve multi-line headers, units, and footnotes, then coerce types with strict parsers and validate ranges. Emit long-form tidy tables to simplify downstream analysis and joins.

Use spaCy transformers or Hugging Face pipelines to tag organizations, chemicals, metrics, and dates, then persist spans with confidences. Route low-confidence items to human review queues.

Store original PDFs, OCR outputs, page images, and parsed JSON with checksums in S3 or GCS. Organize by dataset version and keep a manifest so experiments can be replayed exactly.

Integrate MLflow or Weights & Biases to capture parameters, metrics, plots, and model files automatically. Tag runs with Git SHA, dataset version from DVC or LakeFS, and feature store snapshot IDs.

Build Docker images with deterministic bases, use Poetry or pip-tools lockfiles, and record CUDA or MKL versions. Publish immutable images so jobs run identically in CI and the orchestrator.

Use Papermill or Jupytext to run notebooks with inputs for date ranges and dataset versions, then export HTML or PDF with nbconvert. Keep notebooks clean with pre-commit hooks for outputs and metadata.

Use DuckDB over Parquet for fast local queries and Polars for prototyping transforms, then promote to Spark only when needed. Seed small synthetic datasets to test edge cases in CI.

In Airflow, Dagster, or Prefect, couple each task with unit tests and OpenLineage emissions. Fail fast on schema diffs and route remediation playbooks to owners.

Render Jinja2 templates backed by DuckDB, BigQuery, or Snowflake queries to produce daily and weekly KPIs. Include period-over-period deltas and confidence intervals to avoid overreacting to noise.

Combine rule-based text with statistical tests like contribution analysis and Changepoint detection to explain anomalies. Push summaries into Superset or Looker as annotations or tiles.

Add calibration curves, ROC or PR plots, feature importance or SHAP summaries, and drift charts to dashboards. Provide slice-based performance for fairness and data quality segments.

Use WeasyPrint or ReportLab to combine charts, tables, and narratives, embedding dataset versions, model hashes, and lineage links in the metadata. Keep a changelog that lists breaking metric definition updates.

Track prompt templates and evaluation datasets in Git, and score outputs with promptfoo or TruLens on accuracy, completeness, and citation rate. Guard responses with JSON Schema or pydantic parsing to enforce structure.