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End-To-End MLOps Platform at LINE

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

Tech-Verse2022
PRO

November 17, 2022
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Transcript

  1. None
  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

  51. None
  52. ML Engineer MLOps

  53. ML Engineer Marketer Planner Service Engineer Designer Server Engineer MLU

  54. Thank you