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᮫ԙզ࿨%BUB%FW Ꮣ੒Ոਓతಹ݅ࣄ Data Dev/ Maggie Lee 2023.04.21

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Maggie Lee Machine Learning Engineer @LINE Data Dev 政⼤中⽂學⼠ 雙政⼤資科 Research Assistant @CKIP Lab LINE TECH FRESH @LINE Data Dev Software Graphics intern @Intel CCG 政⼤資科所碩⼠ @LKT lab 害怕⼀鏡到底的⼈⽣ 討厭社會對中⽂系的偏⾒ 連滾帶爬 跌跌撞撞 誤打誤撞 被鄰座強制開啟新世界 跨出舒適圈探索未知 成⻑總是有失有得 沒有極限的研究⽣活 跨領域研究的酸甜苦辣 每天都是⼀場新挑戰 視野⼤展開 努⼒點滿MLE技能 Work Life balance才是終極⽬標

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Data Dev LINE Family Services LINE SHOPPING LINE SPOT LINE MUSIC LINE Sticker LINE VOOM LINE Reward Fact Checker LINE HELP TW LINE Travel NLP CV MarTech NER Classifier Duplica-on Detector Auto comple-on Keyword Extraction Related Search Text Genera-on User Tagging Data Analy,cs Recom- mendation CLV/ RFM LINE TODAY 3 Data Dev的任務有哪些 Image Search Social Intelligence UpliB Modeling Sales forecasting STT

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ML/DL Business Knowledge Data Scientist Data Engineer Data Visualization Statistics Data Dev成員組成 Data Dev Machine Learning Engineer Product Engineer

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Big Data infra Backend Data Scientist Data Engineer SQL ETL Data Dev成員組成 Data Dev Machine Learning Engineer Product Engineer

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MLOPs backend Data Scientist Data Engineer ML/DL ETL Data Dev成員組成 Data Dev Machine Learning Engineer Product Engineer

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DS DE MLE PM Biz DS DE DS DS DE DS MLE Data prepara1on Scaling Performance Model decay Data dri= EDA Model build Hyper-parameter tuning Evalua1on Feature Engineering Error analysis 一個ML專案會經過… MLE MLE MLE DE DS

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MLE的使命是什麼 DEV: Develop/Testing/Deployment OPS: CI/CD/CT

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9 如何實踐MLOPs experiments Source code Pipeline CI Pipeline CD Automated Pipeline Trained model Model CD Con9nuous Training Monitoring Prediction service Develop produc9on Source code pipeline model package

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10 Data preparation Model training Model Evalua2on Model Valida2on Model Analysis Experiment

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11 如何實踐MLOPs experiments Source code Pipeline CI Pipeline CD Automated Pipeline Trained model Model CD Con9nuous Training Monitoring Predic9on service Develop produc9on Source code pipeline model package

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12 experiments Source code Pipeline CI Pipeline CD Automated Pipeline Trained model Model CD Monitoring Predic9on service 如何實踐MLOPs Con9nuous Training Develop production Source code pipeline model package

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13 experiments Source code Pipeline CI Pipeline CD Automated Pipeline Trained model Model CD Monitoring Predic9on service 如何實踐MLOPs Continuous Training Develop produc9on Source code pipeline model package

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14 experiments Source code Pipeline CI Pipeline CD Automated Pipeline Trained model Model CD Monitoring Prediction service 如何實踐MLOPs Con9nuous Training Develop produc9on Source code pipeline model package

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15 Packaging Containerizing Deployment Model Service package管理 Bundle file Docker Image Model file pipeline Object Storage harbor Docker build/push Meta data

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16 experiments Source code Pipeline CI Pipeline CD Automated Pipeline Trained model Model CD Monitoring Prediction service 如何實踐MLOPs Con9nuous Training Develop produc9on

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• 建立儀表板管理資料管道狀態 à 確認ETL是否成功 • 紀錄table的來源及schema à 加速debug的效率 • 針對特定重要指標進行監測(例如:UU/AU) à 確認資料源頭是否異常 Data Observation 一旦出現異常,可即時發送警告信或訊息 除了系統health check 我們還可以做什麼

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18 Case – SmartText portal

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19 如果每次都要自己手動重新訓練好麻煩 有沒有辦法直接拿到現成的模型? 有了良好的模型 要怎麼讓使用者知道並使用呢? With ML background 你們的模型好酷! 但我對機器學習沒有概念 要怎麼應用呢... 可以簡單到用手指頭點一點就好了? With no ML background

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20 如果我們可以做到…

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21 團隊如何分配任務 任務量 當前⼯作量 任務適配度 OKR 任務緊急程度 當前任務優先序

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22 團隊如何分配任務 Objective Key Result 與團隊⽬標有⼀致性/具備影響⼒/有時間限制 Object Improve service stability Key Result 1. Build data observation dashboard for 2+ service in Q3 2. Design outage or service accident info process Example

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23 團隊如何分配任務 任務緊急程度 當前任務優先序 任務適配度 OKR 任務量 當前⼯作量

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24 團隊如何分配任務 任務統計 完成 進行中 待辦事項 5 4 2 優先序⾼ 優先序低 high high low low medium medium medium medium medium low

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25 團隊如何分配任務 任務量 當前⼯作量 任務適配度 OKR 任務緊急程度 當前任務優先序

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26 團隊如何分配任務 Leo 1 4 1 Nina 3 6 1 Maggie 5 4 2

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2"5JNF