Machine Learning Development Team, LINE Machine Learning Dept

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Machine Learning Development Team Wit Tatiyanupanwong 2021.07.01

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1 Mission & Tasks 2 Case (library) ghee-models 3 Case (API) Auto Recommendation 4 We are Hiring! Agenda

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Task ① Python Library ghee / ghee-models / cumin Task ② API Batch Recommendation / Auto Recommendation / Postprocess API Task ③ New Areas Client Side / Federated Learning / Privacy Maximize ML value from LINE compute/data scale Mission Users: ML Engineer, LINE end-user

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ghee-models sklearn at LINE scale Problem – reinventing the wheel ML Part • Classification (DNN for Sparse Input) • Recommendation (DNN, GCN) • Evaluations (precision/recall/NDCG/diversity) Engineering Part • Process Gigabytes/Terabytes of Training Data in parallel • Composite Embedding • Positive/Negative Sampling Methods

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ghee-models

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Auto Recommendation ML Part Problem: Cold Start Item in Recommendation • New Service • New Item (e.g. news article) Solution • User Feature across LINE services • Item Feature (Text + Image) • Challenge: Offline Model Tuning

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Auto Recommendation Engineering Part System Integration • API Design • Throughput • Memory Usage • Error Handling

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ML Recommendation (DNN/GCN) User/Item Representation Learning Client Side, Federated Learning Engineering Hadoop/YARN/Kubernetes Workflow (Azkaban, airflow, argo) Distributed Computing We are Hiring TL;DR: Solid Computer Science and Machine Learning Experience Let’s learn, build and WOW users together