Data engineering
Multi-source acquisition, source timestamps, schemas, caches, lineage, missing-data handling and freshness controls.
Progressive path
From visible output to implementation.
Each level adds detail without forcing the reader to restart: enter at the most useful point and continue progressively.
Machine learning
Weighted rules, supervised models, anomaly detection, regime classification, bootstrap and baselines.
Software engineering
Modular packages, CLI, configuration, typed models, tests, logging, artifacts and versioning.
Automation and RPA
Cron, event triggers, retries, quality gates, idempotency, persistent state and AT Protocol publishing.
Validation
Temporal splits, walk-forward evaluation, matured metrics, baseline comparison, leakage control and failure analysis.
Technical contexts
Energy, markets, technology chains, geospatial data and patterns transferable to industrial and IT/OT systems.
How to verify
Every capability links to concrete evidence.
Technical review or methodological discussion
Email is available for observations on method, possible applications or research.