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좋은 AI 제품은 좋은 모델 + 좋은 UX가 아니라, "제약 안에서 신뢰를 만드는 UX 설계"다. 확정적 UI와 생성적 UI의 경계, 신뢰·불확실성·편집 가능성 디자인, 스트리밍·피드백·실패 UX, 에이전트와 음성의 UX 패턴, 온보딩·접근성·윤리, 한국 문화와 한국어 특수성까지. 엔지니어와 디자이너가 함께 읽는 한 편.
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- Published on
Season 5 Ep 7. 데이터가 많아질수록 "누가·어디서·왜·얼마나" 쓰는지 불명확해진다. OpenLineage 표준, 데이터 카탈로그 4대(Collibra·Atlan·DataHub·Alation), Unity Catalog·Polaris·Glue의 거버넌스, PII 탐지·마스킹·토큰화, GDPR·한국 개인정보보호법의 데이터 주체 권리, 멀티 클라우드 거버넌스, AI 시대의 거버넌스 확장, 한국 기업 현실까지.- Published on
Season 5 Ep 4. 엔진이 쿼리한다면, 오케스트레이터는 파이프라인을 다스린다. dbt의 표준화와 한계, SQLMesh의 반란, Dagster의 asset-centric 모델, Airflow 2.x/3.0의 진화, Prefect 3.0의 재탄생, 데이터 계약(Data Contracts), 파이프라인 CI/CD, 관측성과 경보, 한국 기업 실무 스택 선택까지.- Published on
Season 5 Ep 6. ML의 언어는 피처, AI의 언어는 벡터, 관계의 언어는 그래프, 운영의 언어는 시계열. 2025년 각 DB 카테고리가 서로 경계를 넘고 Lakehouse·Postgres 위에 수렴한다. Feature Store 재정의, Online-Offline skew, Vector DB 지형, Graph DB와 지식 그래프, 시계열 DB의 현주소, "Unified DB"의 등장, AI가 DB 지형에 준 충격, 한국 기업의 실무 선택까지.- Published on
"Fine-tuning은 죽었다"는 소리도, "RAG로 다 된다"는 소리도 반쯤만 맞다. 2025년 시점에서 SFT/DPO/RLHF의 지형, LoRA·QLoRA로 가볍게 튜닝하는 실전, 합성 데이터 파이프라인, 한국어 모델(Solar, Qwen, Llama) 파인튜닝, 그리고 Fine-tune vs RAG vs Prompt의 경계선을 한 글로 정리합니다.- Published on
Season 4의 마지막 글. 기술·운영·디자인을 다 갖춰도 비즈니스 모델이 없으면 1년짜리 제품이다. 가격 모델 5종(Subscription/Usage/Hybrid/Seat/Outcome), 비용 구조와 마진, GTM(B2C·B2B·Enterprise·Prosumer), 데이터 플라이휠·해자, "AI wrapper" 비판을 넘는 법, 규제·평판 리스크, 투자 지형, M&A·통합, 한국 스타트업의 글로벌 진출, Season 4 회고와 Season 5 예고. 엔지니어·PM·창업자가 한 번에 읽는 한 편.- Published on
레이크하우스 2025 완전 가이드: Iceberg vs Delta vs Hudi, Open Table Format 전쟁, Snowflake·Databricks 합종연횡 (2025)
Season 5 Ep 1. 2024년 Snowflake가 Iceberg를 네이티브 지원하고 Databricks가 Tabular를 $1B+로 인수하면서 "Open Table Format 전쟁"은 끝났다. 남은 건 Iceberg를 어떻게 잘 쓸 것인가. Iceberg·Delta·Hudi 아키텍처 비교, Parquet·ORC·Puffin 내부, 카탈로그 전쟁(Glue/Polaris/Unity/Nessie), 성능 튜닝, 마이그레이션, 한국 기업의 실무 의사결정까지.- Published on
2024년 "에이전트의 해" 다음으로 온 2025년은 "에이전트가 제품이 된 해"다. ReAct와 Plan-Execute의 경계, Tool use 설계 원칙, LangGraph 상태 머신, 다중 에이전트 오케스트레이션, MCP와 에이전트의 관계, 그리고 실패 루프와 보안까지. 현업 관점으로 정리한 한 편.- Published on
모델을 바꿨더니 더 좋아진 것 "같다"는 주장은 과학이 아니다. 2025년 LLM 제품의 실패 원인 1위는 "측정 안 함". Eval harness 설계, LLM-as-judge 함정과 보정, Trace·Span·Metric 3층 관측성, 프로덕션 피드백 루프, Phoenix·LangSmith·LangFuse·Helicone 비교까지. 평가를 제품 개발의 근간으로 세우는 법.- Published on
2024–2025년 LLM 제품의 실패 원인 TOP3 안에 항상 "보안 사고"가 들어간다. Prompt injection 12변종, Jailbreak 기법, Data exfiltration, Model extraction, Red team 자동화(PyRIT/Garak), OWASP LLM Top 10, EU AI Act와 한국 규제, 그리고 가드레일 설계까지. "기본값으로 안전한 LLM 제품"을 만드는 법.- Published on
LLM 제품을 빠르게 만드는 법은 쉬워졌다. 지속 가능하게 돌리는 법이 어렵다. 모델·프롬프트·평가셋의 3축 버전 관리, Shadow/Canary/Blue-Green 배포, 토큰·캐시·모델 라우팅으로 비용 통제, 조직 구조(AI 플랫폼/Model 플랫폼/Product AI), 실패 사례, KPI·온콜까지. MLOps의 연장선이되, LLM 고유의 과제를 정면으로 다루는 한 편.- Published on
외부 API에 모든 걸 의존하던 시절이 끝나간다. 2025년은 소비자 GPU와 Apple Silicon에서 30B–70B 모델이 "쓸 만하게" 돌아가는 시대. 모델 선택(Llama/Qwen/Mistral/Gemma/Solar), 엔진(vLLM/TGI/SGLang/llama.cpp/Ollama), 양자화(AWQ/GPTQ/EXL2/GGUF), 하드웨어, 비용·전력, 그리고 Privacy-first 제품의 실전까지.- Published on
Anthropic이 2024년 말 공개하고 2025년에 사실상 표준이 된 MCP. Resources·Tools·Prompts·Sampling 스펙과 전송 계층, OAuth 2.1 인증, 주요 서버(GitHub/Slack/Linear/Chrome/Playwright), 직접 서버 만들기, 보안 공격 벡터, 엔터프라이즈 운영까지. "LLM 시대의 USB-C"를 한 글로 해부합니다.- Published on
텍스트만 다루던 시대가 끝났다. 2025년 LLM은 이미지·문서·영상·오디오를 자연스럽게 처리한다. GPT-4o/Claude 3.5/Gemini/Qwen2-VL/Pixtral 비교, Document AI와 레이아웃 이해, OCR의 현대화, 비디오·오디오, 한국어 문서 특수성, 그리고 멀티모달 RAG까지. 실전 케이스로 정리한 한 편.- Published on
Season 5 Ep 8. 관측성 없이는 운영 없고, 운영 없이는 제품 없다. OpenTelemetry의 3대 시그널(Metric·Log·Trace) 통합, Grafana 스택(Prometheus·Loki·Tempo·Mimir) vs Datadog·New Relic·Splunk, SigNoz·Honeycomb·Axiom의 새 세대, SLO·SLI·Error Budget 운영, LLM 관측성(LangFuse·LangSmith·Phoenix·Helicone), 한국 기업의 관측성 스택, "관측성은 제품 품질"이라는 선언까지.- Published on
Season 5 Ep 3. 하나의 엔진이 모든 OLAP을 커버하지 않는다. DuckDB는 단일 노드의 혁명을, ClickHouse는 실시간 OLAP을, Snowflake·BigQuery는 관리형 편리함을, StarRocks·Doris·Pinot·Druid는 실시간 MPP를, Trino는 연합 쿼리를 맡는다. 각 엔진의 강약점, 벤치마크의 함정, 적재적소 배치 패턴, 한국 기업 선택 가이드까지.- Published on
2023년엔 "주문"이었고, 2024년엔 "기술"이었고, 2025년엔 "과학"이 된 프롬프트 엔지니어링. Chain-of-Thought, Self-consistency, Tree-of-Thoughts, DSPy 자동 최적화, Structured Output, 프롬프트 버저닝, 모델 간 이관까지. 실전 운영 관점에서 한 글로 정리합니다.- Published on
LLM에 외부 지식을 붙이는 가장 실용적인 패턴, RAG. 2025년 시점에서 임베딩 모델 선택, 청킹 전략, 벡터 DB 선택, Rerank, Hybrid Search, 그리고 Fine-tuning과의 경계까지. Notion AI와 Claude의 검색 아키텍처를 해부하고, 한국어 RAG의 특수성과 비용·지연시간 최적화 체크리스트를 한 호흡에 정리합니다.- Published on
Season 5 Ep 5. 파이프라인이 데이터를 만든다면, Semantic Layer는 데이터의 의미를 말한다. dbt Semantic Layer·Cube·AtScale·Looker의 계보, Metrics Store 패턴, Headless BI(Lightdash·Transform), Reverse ETL(Hightouch·Census)로 데이터를 운영 도구로 되돌리는 흐름, Data activation 전략, AI와 Semantic Layer의 만남, 한국 기업의 성숙도까지.- Published on
Season 5 Ep 2. "모든 데이터를 실시간으로"는 2020년의 약속이었다. 2025년은 실용주의의 시대 — 배치와 스트리밍의 경계는 흐려졌고, 진짜 질문은 "이 데이터는 얼마나 신선해야 SLA를 만족하는가"로 바뀌었다. Flink·RisingWave·Materialize·ksqlDB 비교, Kappa/Lambda의 2025년 버전, Iceberg v3 row-level delete와 실시간 upsert, CDC 파이프라인, 비용·지연 트레이드오프, 한국 실무 사례까지.- Published on
"화면 없는 AI"가 2025년의 가장 뜨거운 제품 범주가 된 이유. 실시간 음성 파이프라인(VAD/STT/LLM/TTS), 음성 LLM(GPT-4o realtime/Gemini Live/Moshi), Turn-taking과 인터럽션, 감정·억양 제어, 전화·브라우저·모바일 실전, 딥페이크 방어와 보안, 한국어 음성 제품의 특수성까지.