인공지능 및 기계 학습 프로젝트들은 데이터 유입부터 학습, 모델 검증 및 제공까지 전체 프로세스의 반복을 통해 최적의 값을 지속적으로 제공하는 것을 목표로 합니다. 본 실습에서는 Amazon Forecast가 지속적으로 데이터를 학습하고 시계열 예측 모델을 제공할 수 있도록 자동화 된 MLOps 파이프라인을 구축하는 방법에 대해 학습합니다.

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시계열 예측 자동화를 위한 Amazon
Forecast 기반 MLOps 파이프라인 구축하기
김주영
솔루션즈 아키텍트
AWS
이동민
솔루션즈 아키텍트
AWS

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AWS ML Stack Overview
Amazon Forecast Overview
MLOps with AWS Services
Hands on Lab Overview
Hands on Lab Demo
Agenda

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AWS ML Stack Overview

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The AWS ML Stack
VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD CONTACT CENTERS
Deep
Learning
AMIs &
Containers
GPUs &
CPUs
Elastic
Inference
Trainium Inferentia FPGA
AI SERVICES
ML SERVICES
FRAMEWORKS & INFRASTRUCTURE
DeepGraphLibrary
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Comprehend
+Medical
Amazon
Textract
Amazon
Kendra
Amazon
CodeGuru
Amazon
Fraud Detector
Amazon
Translate
INDUSTRIAL AI CODE AND DEVOPS
NEW
Amazon
DevOps Guru
Voice ID
For Amazon Connect
Contact Lens
NEW
Amazon
Monitron
NEW
AWS Panorama
+ Appliance
NEW
Amazon Lookout
for Vision
NEW
Amazon Lookout
for Equipment
NEW
Amazon
HealthLake
HEALTH AI
NEW
Amazon Lookout
for Metrics
ANOMALY DETECTION
Amazon
Transcribe
Medical
Amazon
Comprehend
Medical
Amazon
SageMaker
Label
data
NEW
Aggregate &
prepare data
NEW
Store & share
features
Auto ML Spark/R
NEW
Detect
bias
Visualize in
notebooks
Pick
algorithm
Train
models
Tune
parameters
NEW
Debug &
profile
Deploy in
production
Manage
& monitor
NEW
CI/CD
Human
review
NEW: Model management for edge devices
NEW: SageMaker JumpStart
SAGEMAKER STUDIO IDE

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Amazon Forecast Overview

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Amazon Forecast
정확한 예측을 위한 자동화된 기계 학습 서비스
완전 관리형
데이터 파이프라인, 학습,
예측의 구성 자동화
높은 예측률
전통적인 모델 대비
예측 정확도 50% 향상
쉬운 사용법
딥러닝 경험 불필요
안심 가능한 보안
AWS KMS 통한 암호화

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Amazon Forecast
Customized
forecasting API
Inspect
data
Identify
features
Select most
accurate model
from multiple
algorithms
Select
Hyper-
parameters
Host
models
Load
data
Train
models using
multiple
algorithms
Optimize
models
Amazon Forecast
Fully managed by Amazon Forecast
Historical data
Related data
sales, call volume, inventory,
resource demand.
Price, promotions, weather
data, custom events
Item metadata
Color, city, country, category,
author, album name
Create dataset Create predictor
(train, inference, metrics)
Create
Forecast
Create Forecast
Export Query Forecast

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MLOps with AWS Services

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MLOps
AI/ML
Services
Step
Functions
Native integration
or via Lambda
S3
Data
Data
Data
Processing
Model
Training
Model
Deployment
Building automated ML workflows
People
Process
Technology

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Hands on Lab Overview

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https://main.dglgxgyeuxutx.amplifyapp.com/
Hands on Lab 링크

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Hands on Lab – Amazon Forecast 맛보기
Amazon SageMaker
사용자
notebook
SDK
데이터 전처리 Amazon Forecast
원본 데이터 전처리된 데이터

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Hands on Lab – MLOps 파이프라인 구축하기
AWS Cloud

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STEP 1
CREATE DATASET
STEP 2
CREATE DATSETGROUP
STEP 3
IMPORT DATA
STEP 6
UPDATE RESOURCES
STEP 5
CREATE FORECAST
STEP 4
CREATE PREDICTOR
STEP 7
NOTIFY SUCCESS
STEP 8
STRATEGY CHOICE
STEP 9
SUCCESS STATE
START
END
AWS Step Functions를 사용하여 MLOps 파이프라인 만들기
MLOps 파이프라인

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Hands on Lab Demo

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