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LLM을 프로토타입에서 프로덕션으로 가져가는 여정은 Jupyter 노트북의 데모와 다르다. RAG 파이프라인 설계, eval harness 구축, fine-tuning 결정 트리, LLMOps 관측 가능성, Guardrails와 안전성, Prompt Caching과 비용 최적화까지 2025-2026년 AI 엔지니어의 현장 가이드를 500줄로 정리한다.
Cost-optimization
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Kubernetes 환경에서 FinOps 원칙을 적용하여 클라우드 비용을 최적화하는 전략을 다룹니다. Kubecost, OpenCost 도입부터 리소스 튜닝, Spot 인스턴스 활용, Karpenter 기반 자동 스케일링까지 실전 가이드를 제공합니다.- Published on
A practical guide to tackling observability cost explosion. Covers OpenTelemetry Collector-based sampling policies, log filtering pipelines, metric cardinality management, storage tiering architecture, and a phased optimization checklist.- Published on
Observabilityデータ急増に伴うコスト課題を解決する実践ガイド。OpenTelemetry Collectorベースのサンプリング戦略、ログフィルタリング、メトリクスカーディナリティ管理、ストレージティアリングを実例とチェックリスト付きで解説します。- Published on
LLM 라우팅과 캐스케이드 전략으로 비용을 최적화하는 방법을 다룹니다. 쿼리 복잡도 기반 라우팅, 모델 캐스케이드, 시맨틱 라우터, FrugalGPT 접근법부터 프로덕션 멀티 모델 오케스트레이션 아키텍처까지 구현합니다.- Published on
Observability 데이터 폭증에 따른 비용 문제를 해결하는 전략 가이드. OpenTelemetry Collector 기반 샘플링 정책, 로그 필터링 파이프라인, 메트릭 카디널리티 관리, 스토리지 티어링 아키텍처와 비용 절감 사례를 다룹니다.