기술스터디 210623 김성익

1. 모델 서빙 파이프라인 구축하기
1
김성익

2. 목차
1. Task Summary
2. Web Serving? Model Serving?
3. Conceptualize
4. Containerize
5. Model to API
6. Workflow Management
7. Architecture Design
8. Demo
2

3. DKT - Deep Knowledge Tracing
3
1. Task Summary
3
사용자의 Interaction 데이터를 이용하여
사용자의 "지식 상태"를 추적하는 딥러닝 방법론
주어진 마지막 문제를 정답여부를 분류하는 이진 분류 문제

4. Data
4
4
예측해야 할 문항!
1. Task Summary

5. 2. Web Serving? Model Serving?

6. 2. Web Serving? Model Serving?

7. Training-Serving Pattern

8. 8
3. Conceptualize

9. 9
3. Conceptualize

10. 10
Client App
Inference App
Image
Image
Image Hub
Client
Container
Inference
Container
4. Containerize

11. 11
5. Model to API Server

12. 12
5-1. Why BentoML?

13. 13
5-1. Why BentoML?

14. 14
6. Workflow Management

15. Retraining
workflow
P40 GPU Server
Pass new data
(request)
Updated data
Rendering
(response)
Get
most recent data
New
trained model
Get
most recent model
Packing
Docker
build&push
Rolling update
in zero downtime
Inference Inference
Inference
Inference Inference
Inference
Response
Request
Loadbalancing
7. Architecture Design

16. 16
8. Demo
Airflow 스케줄링 S3 데이터 Versioning
Docker Swarm 컨테이너 관리