End-To-End MLOps Platform at LINE

Sun Hyeong Hong (LINE Plus / MLU Core TF / Platform Product Manager)

https://tech-verse.me/ja/sessions/49
https://tech-verse.me/en/sessions/49
https://tech-verse.me/ko/sessions/49

Transcript

2. Sunhyeong hong (홍선형) What to eat Today MLU Beginner planner Golf Learning English Travel Platform Product Manager What to drink Today
3. Intro Jason Allen's art created on Midjourney titled “Space Opera Theater”
4. Intro Rain Go Out Stay In Too Hot Cable Signal? Shopping Movie Coffee Shop TV Shows Mode to predict Hong’s Weekend Plan No Yes
5. Intro Is it enough? ML-Model development
6. Intro Data sourcing Streaming Sourcing Ingestion Data management Validation Analysis Segmentation Feature engineering ML-Model development Building Training Evaluation Versioning metadata Application Model deployment Model monitoring Serving API/UI Load balancing Infrastructure Configuration Containerization Logging monitoring CI/CD Pipelines Authentication Hardware/GPUs No. you have to do all this
7. Intro These are the skills you need to do ML Business Programming Statices, ML/AI Communication The business guy The “if” guy The hacker The number cruncher The data translator The ML/AI engineer The perfect Data scientist The salesperson The Data nerd The statistician The Storyteller The good consultant The competence Science professor
8. Intro These are the skills you need to do ML Business Programming Statices, ML/AI Communication Are you okay...?
9. Agenda - What is Machine Learning Pipeline? - How to Build an End-to-End a Machine Learning Pipeline? - Building an End-to-End Pipeline using MLU - Next MLU?
10. What is Machine Learning Pipeline?
11. How to Build an End-to-End a Machine Learning Pipeline? Data Extraction and Analysis Data Preparation Model Training Model Evaluation and Validation Trained Model Manual experiment steps Offline data Manual ML Pipeline
12. How to Build an End-to-End a Machine Learning Pipeline? Data Extraction and Analysis Data Preparation Model Training Model Evaluation and Validation Model Serving Trained Model Manual experiment steps Offline data Model registry Ops Manual ML Pipeline ML
13. How to Build an End-to-End a Machine Learning Pipeline? It's a new model ML Ops
14. How to Build an End-to-End a Machine Learning Pipeline? It's a new model I checked, but It doesn’t work ML Ops
15. How to Build an End-to-End a Machine Learning Pipeline? It's a new model What? I have no problem I checked, but It doesn’t work ML Ops
16. How to Build an End-to-End a Machine Learning Pipeline? It's a new model What? I have no problem I checked, but It doesn’t work what python do you use? ML Ops
17. How to Build an End-to-End a Machine Learning Pipeline? It's a new model What? I have no problem Ah, My Python version is not compatible , so I uploaded it to 3.8. I checked, but It doesn’t work what python do you use? ML Ops
18. How to Build an End-to-End a Machine Learning Pipeline? It's a new model What? I have no problem Ah, My Python version is not compatible , so I uploaded it to 3.8. I checked, but It doesn’t work what python do you use? Oh… Please send requirements.txt ML Ops
19. How to Build an End-to-End a Machine Learning Pipeline? Automation ML pipeline Data Extraction Data Preparation Model Training Model Evaluation Model Serving Trained Model Code Repository Model registry Model Monitoring Data Validation Model Validation Feature Store Data Analysis And Experimentation Now Code Model registry
20. How to Build an End-to-End a Machine Learning Pipeline? Automation ML pipeline Data Extraction Data Preparation Model Training Model Evaluation Model Serving Trained Model Automated pipeline Code Repository Model registry Model Monitoring Data Validation Model Validation Feature Store Data Analysis And Experimentation Now Code Model registry
21. How to Build an End-to-End a Machine Learning Pipeline? Automation ML pipeline Data Extraction Data Preparation Model Training Model Evaluation Model Serving Trained Model Automated pipeline Code Repository Model registry Model Monitoring Data Validation Model Validation Feature Store Data Analysis And Experimentation Now Code Model registry
22. LINE's MLOps platform Machine Learning Universe
23. Building an End-to-End Pipeline using MLU VOOM For you recommendation system
24. Machine Learning Model Building an End-to-End Pipeline using MLU VOOM For you recommendation system VOOM public Post Create a recommended candidate group Recommendation candidate data filtering and quality verification Recommendation model Training
25. Machine Learning Model Building an End-to-End Pipeline using MLU VOOM For you recommendation system Create a recommended candidate group Recommendation candidate data filtering and quality verification Recommendation model Training Recommendation model deployment VOOM public Post Recommendation model monitoring
26. Machine Learning Model Building an End-to-End Pipeline using MLU VOOM For you recommendation system Create a recommended candidate group Recommendation candidate data filtering and quality verification Recommendation model Training Recommendation model deployment VOOM public Post User Feedback Recommendation model monitoring
27. Continuous Data and Model updates
28. ML Model Logging and Tracking
29. ML Model API Packaging
30. Server resource optimization
31. Different development environment
32. MLU
33. Building an End-to-End Pipeline using MLU The VOOM recommendation model process within the MLU architecture Data preprocessing Model training Feature Store Data Interactive Computing Mountable Filesystem Model validation
34. Building an End-to-End Pipeline using MLU The VOOM recommendation model process within the MLU architecture Data preprocessing Model training Feature Store Data Interactive Computing Experiment Management Mountable Filesystem Model validation
35. Building an End-to-End Pipeline using MLU The VOOM recommendation model process within the MLU architecture Data preprocessing Model training Feature Store Data Interactive Computing Experiment Management Mountable Filesystem Model validation Model packaging Model deployment Packaging Management
36. Building an End-to-End Pipeline using MLU The VOOM recommendation model process within the MLU architecture Data preprocessing Model training Feature Store Data Interactive Computing Experiment Management Mountable Filesystem Model validation Model packaging Model deployment Workflow Management Packaging Management
37. Building an End-to-End Pipeline using MLU The VOOM recommendation model process within the MLU architecture Data preprocessing Model training Feature Store Data Interactive Computing Experiment Management Mountable Filesystem Model validation Model packaging Model deployment Workflow Management Packaging Management Model Serving Model Monitoring Serving Management
38. Building an End-to-End Pipeline using MLU The dashboard of the MLU portal
39. Building an End-to-End Pipeline using MLU Monitoring screen of MLU serving
40. Building an End-to-End Pipeline using MLU Pipeline operation accidents 90%↓ 100%↓ ! 95%↓ !
41. Building an End-to-End Pipeline using MLU Improve model performance CTR 25% → 38% (13%↑) Pipeline operation accidents 90%↓ 100%↓ ! 95%↓ !
42. Building an End-to-End Pipeline using MLU Provisioning
43. Provisioning ML Ecosystem Minimal Code Building an End-to-End Pipeline using MLU
44. Building an End-to-End Pipeline using MLU Provisioning ML Ecosystem GUI Platform
45. Building an End-to-End Pipeline using MLU MLU Usage Status - As of October ‘22 Cluster GPU: 504 Cores CPU: 14,886 Cores MEM: 52TB
46. Building an End-to-End Pipeline using MLU MLU Usage Status - As of October ‘22 Cluster GPU: 504 Cores CPU: 14,886 Cores MEM: 52TB User Total Count: 1,111 Company: 33 Team: 386
47. Building an End-to-End Pipeline using MLU MLU Usage Status - As of October ‘22 Cluster GPU: 504 Cores CPU: 14,886 Cores MEM: 52TB User Total Count: 1,111 Company: 33 Team: 386 Pipeline Pipeline : 300 Serving : 107
48. - Efficient Data management tools - An active community where everyone can share their knowledge - A Public repository for sharing Models and Datasets Next MLU
49. - Efficient Data management tools - An active community where everyone can share their knowledge - A Public repository for sharing Models and Datasets Next MLU
50. Released just yesterday MLU MARKET PLACE
52. ML Engineer MLOps
53. ML Engineer Marketer Planner Service Engineer Designer Server Engineer MLU
54. Thank you