Cert Notes/ 출퇴근 학습 노트
로드맵
KOEN
CLF-C02 · FoundationalCloud Practitioner - Foundational
DVA-C02 · AssociateDeveloper - Associate
SAA-C03 · AssociateSolutions Architect - Associate
SOA-C02 · AssociateCloudOps Engineer - Associate
SAP-C02 · ProfessionalSolutions Architect - Professional
DOP-C02 · ProfessionalDevOps Engineer - Professional
SCS-C03 · SpecialtySecurity - Specialty
MLA-C01 · AssociateMachine Learning Engineer - Associate
AIF-C01 · FoundationalAI Practitioner - Foundational
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
  • Week 1
    • 1.ML Lifecycle (Specialty Perspective)
    • 2.ML용 데이터 저장소: S3·EFS·FSx for Lustre·데이터 포맷
    • 3.데이터 수집: Kinesis·Glue·배치 vs 스트리밍
    • 4.Data Labeling: SageMaker Ground Truth·Active Learning·Label Quality
    • 5.Week 1 Integrated Review: ML Overview & Data Engineering 1
  • Week 2
    • 1.데이터 변환과 ETL: AWS Glue, Spark, 그리고 EMR
    • 2.학습 데이터 파이프라인 자동화: Step Functions와 SageMaker Pipelines
    • 3.Data Augmentation and Synthesis: Addressing Insufficient and Imbalanced Data
    • 4.Data Storage and Access Optimization: Pipe vs File Mode, FSx for Lustre, Distributed Training
    • 5.Week 2 Comprehensive Review: From Transformation to Distributed Training Data Supply
  • Week 3
    • 1.Data Cleaning: Missing Values, Outlier Detection, Duplicates and Errors
    • 2.특성 공학: 스케일링, 인코딩, 비닝
    • 3.Time Series, Text Features, and High-Cardinality Categorical Handling
    • 4.SageMaker 도구: Data Wrangler, Processing Job, Feature Store
    • 5.Week 3 Comprehensive Review: Cleaning and Feature Engineering
  • Week 4
    • 1.차원 축소: PCA, t-SNE, 차원의 저주
    • 2.Feature Selection: Filter, Wrapper, Embedded, Importance, Multicollinearity
    • 3.Data Visualization: Distribution, Correlation, QuickSight, Insights
    • 4.Handling Class Imbalance: Over/Undersampling, SMOTE, Class Weights, Evaluation
    • 5.Week 4 Comprehensive Review: Dimensionality, Feature Selection, Visualization, Imbalance
  • Week 5
    • 1.Statistical Foundations: Distribution, Central Tendency, Dispersion, Transformations, Sample and Population
    • 2.Correlation and Relationships: Correlation Coefficients, Causation vs. Correlation, Multivariate Relationships
    • 3.Data Leakage: Causes, Detection, Prevention; Time Series Leakage; Target Leakage
    • 4.Validation Design: train/validation/test Split, Cross-Validation, Time Series Split, Stratified Sampling
    • 5.Week 5 Comprehensive Review: Statistics and Validation Design
  • Week 6
    • 1.알고리즘 선택: 문제 유형별 매핑
    • 2.SageMaker Builtin 1: XGBoost, Linear Learner, K-Means, KNN
    • 3.SageMaker Builtin 2: Text, Image, Time Series, Recommendation
    • 4.Unsupervised/Anomaly Detection: RCF, PCA, IP Insights, Topic Models (LDA/NTM)
    • 5.Week 6 Comprehensive Review: Algorithm Selection and SageMaker Builtins
  • Week 7
    • 1.신경망 기초: 퍼셉트론에서 역전파까지
    • 2.CNN: Convolutional Neural Networks and Computer Vision
    • 3.RNNs and Sequences: From LSTM to Transformer
    • 4.학습 기법과 전이학습
    • 5.Week 7 Synthesis: Deep Learning Summary
  • Week 8
    • 1.SageMaker 학습 작업: Estimator, 입력 모드, 분산 학습, Spot
    • 2.하이퍼파라미터 튜닝(AMT): 베이지안·랜덤·Hyperband
    • 3.Overfitting/Underfitting: Diagnosis and Regularization/Data Augmentation
    • 4.학습 최적화: 배치 크기·학습률, 그래디언트 문제, Debugger/Profiler
    • 5.Week 8 Review: Training, Tuning, Generalization
  • Week 9
    • 1.분류 평가지표: 정확도·정밀도·재현율·F1과 혼동행렬
    • 2.ROC/AUC와 임계값 조정: 곡선으로 읽는 모델 성능
    • 3.회귀 평가지표: RMSE·MAE·MAPE·R²와 잔차 분석
    • 4.모델 디버깅과 편향: SageMaker Debugger와 Clarify
    • 5.Week 9 종합 복습: 평가와 디버깅
  • Week 10
    • 1.추론 옵션: 실시간 vs 서버리스 vs 비동기 vs 배치 변환
    • 2.Real-time Endpoint Operations: Configuration, Auto Scaling, Multi-Model
    • 3.Inference Optimization: Neo, Elastic Inference, Inferentia, Inference Pipelines
    • 4.Deployment Strategies: A/B Testing, Blue/Green, Canary, Shadow, Rollback
    • 5.Week 10 Review: ML Implementation & Operations 1 — Deployment & Inference
  • Week 11
    • 1.Model Monitoring: SageMaker Model Monitor and Drift Response
    • 2.MLOps: SageMaker Pipelines, Model Registry, CI/CD
    • 3.ML Security: IAM Execution Roles, VPC Isolation, KMS Encryption
    • 4.Operations & Cost: Cost Optimization, Logging/Audit, Disaster Recovery
    • 5.Week 11 Review: Monitoring, MLOps, Security, Operations
  • Week 12
    • 1.도메인 1·2 통합 복습: 데이터 엔지니어링 + 탐색적 데이터 분석
    • 2.도메인 3 통합 복습: 모델링(알고리즘·딥러닝·튜닝·평가)
    • 3.도메인 4 + 전체 종합: ML 구현·운영 복습 + 4도메인 교차
    • 4.Synthesis: 4 Domains + End-to-End Scenarios
    • 5.D-Day 마무리: 시험 구성·시간 배분·요구사항 번역표·함정 총정리
합격 후기
← MLS-C01/Week 11/Day 4
MLS-C01· AssociateWeek 11 · Day 4읽기 약 14분

Day 4 - Operations & Cost: Cost Optimization, Logging/Audit, Disaster Recovery

📌 핵심 정리

  • 운영은 비용·감사·복원력 세 기둥이다. ML 워크플로는 데이터 파이프라인·학습 GPU·상시 엔드포인트에서 계속 돈이 나간다.
  • 학습과 추론의 경제학은 정반대다. 학습은 **중단 감내(Spot)**로 깎고, 추론은 유휴 비용 제거로 깎는다.
  • Managed Spot Training은 최대 90% 절감이지만 체크포인트 저장이 사실상 필수다.
  • "왜 실패했나·얼마나 느린가"는 CloudWatch, "누가 언제 무엇을 했나"는 CloudTrail이다.
  • DR은 데이터 → 모델 → 엔드포인트 세 자산을 각각 계획한다. 엔드포인트는 코드(IaC) + Model Registry로 재생성 가능해야 한다.

비용 최적화: 학습과 추론은 경제학이 다르다

여기부터는 Pro 전용입니다

Week 1은 누구나 무료로 볼 수 있어요. Week 2부터의 전체 학습 자료와 모의고사·무제한 복습은 Pro 플랜에서 이용할 수 있습니다.

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이전ML Security: IAM Execution Roles, VPC Isolation, KMS EncryptionWeek 11 · Day 3다음 Week 11 Review: Monitoring, MLOps, Security, OperationsWeek 11 · Day 5