How-Tos
Procedural guides for building, setting up, and operating data and AI systems.
Airflow Docker Compose: Local Setup for Data Pipeline Projects
A practical setup for running Airflow locally with Docker Compose for data pipeline projects, with DAG structure, mounted code, checks, logs, and limits.
DataOps Checks for Data Pipelines
A practical checklist for adding DataOps checks to data pipelines: freshness, volume, schema, distribution, uniqueness, business rules, CI/CD, runbooks, and recovery.
How to Build Data Pipelines That People Can Trust
A guide to building data pipelines with ingestion, transformation, orchestration, contracts, testing, observability, and last-mile activation.
How to Run a RAG Evaluation Workflow
A practical workflow for evaluating RAG systems with user tasks, gold examples, retrieval checks, answer checks, citations, human review, traces, and production feedback.
How to Take an AI Notebook to Production
A procedural guide for turning an AI or ML notebook into a production system with scoped business requirements, reproducible code, data paths, evaluation, serving, monitoring, and feedback.