data quality

Practical, bash-first checklists for standing up, tuning, and operating AI data stores on Linux using PostgreSQL + pgvector: portable apt/dnf/zypper installs, disk/CPU baselines (fio/sysstat), schema/versioning for embeddings, ANN indexes (ivfflat/hnsw) and probes, PG/OS tuning, automated backups with restore drills, and observability/drift checks—plus scripts you can copy-paste.

Practical, Bash-first guide to migrate text from a relational DB into an AI-ready vector database for semantic search/RAG. Covers Linux setup, exporting to NDJSON, cleaning and chunking, generating embeddings (sentence-transformers), loading into Qdrant, validating queries and filters, and executing a zero‑downtime blue/green cutover, with troubleshooting and next-step tips.

Practical, Bash-first guide to AI observability on Linux: why ML needs more than CPU/HTTP metrics, how to define AI-specific SLIs/SLOs, emit JSON logs, push Prometheus metrics via Pushgateway, build Grafana dashboards, deploy canary/shadow releases, track drift, and wire alerts for latency, errors, cost, and safety—with copy-paste installs, scripts, PromQL, and an end-to-end test.