Introduction of Machine Learning1 Team

菊地 悠/Machine Learning1チーム/マネージャー

2020.06.24
LINE Data Labs 採用説明会 〜機械学習・データ分析〜
https://line.connpass.com/event/174842/

Transcript

1. MACHINE LEARNING1 TEAM Haruka Kikuchi
2. Agenda • Mission • Project, System, Tool • Position • Team Members' Voices • Reference
3. MISSION
4. MISSION STATEMENT • ػցֶशΛ௨ͯ͡ɺLINEͷ༷ʑͳαʔϏεͷڝ૪ྗɾརӹʹߩݙ͢Δɹɹɹ Provide service competencies and profits through ML solutions to various LINE services • ػցֶशʹؔ͢ΔLINEࣾ಺Ͱͷඪ४ԽɾຽओԽͷਪਐʢάϧʔϓձࣾΛؚΉʣ Lead standardization/democratization process of ML use at scale across LINE (inc. group companies) together w/ other data-related teams/depts.
5. PROJECT, SYSTEM, TOOL
6. Item2Item User2Item LINEελϯϓͷϨίϝϯυ
7. ଟ༷ͳαʔϏε΁ͷΤϯδϯఏڙ Sticker, etc. Manga NEWS Live Parttime Fortune-telling Music Store ࠶ར༻ՄೳͳMLΤϯδϯ ʴ ಋೖ࣌ͷݸผνϡʔχϯά ※աڈʹ։ൃͨ͠αʔϏεʢൈਮʣ
8. ελϯϓը૾΁ͷλάͷࣗಈ෇༩ ਂ૚ֶशϕʔεͷ෼ྨ໰୊
9. ը૾ͷྨࣅ౓Λར༻ͨ͠Ϩίϝϯυ
10. εϚʔτνϟϯωϧ • αʔϏεԣஅͷίϯςϯπΛڞ௨ͷදࣔྖҬʹ഑৴ • ݸผαʔϏεͷ௨஌΍ϨίϝϯυΤϯδϯ + ίϯςϯπԣஅͰͷ഑৴࠷దԽ • ݸผαʔϏεͷΤϯδϯ։ൃ͸ࣾ಺ΦʔϓϯԽͷ࿮૊ΈΛ੔උ • ഑৴࠷దԽ͸ɺෳ਺ͷContextual banditsΞϧΰϦζϜΛఏڙ
11. FEATURE AS A SERVICE • ML༻్ͰͷαʔϏεԣஅσʔλͷඪ४తͳ؅ཧͷ࢓૊Έ
12. 2018೥10݄࣌఺ SYSTEM OVERVIEW ࣄۀผͷγεςϜ ML1 TEAMͷ؅ཧ͢ΔγεςϜ ࣾ಺σʔλج൫γεςϜ
13. 2020೥6݄࣌఺ SYSTEM OVERVIEW Hadoop 3.x k8s port to external system ࣄۀผͷγεςϜ ML1 TEAMͷ؅ཧ͢ΔγεςϜ ࣾ಺σʔλج൫γεςϜ
14. AB TEST TOOLSET • ML LogicվળͷͨΊɺνʔϜ಺ͷ಺੡։ൃ͔Βελʔτʢޙʹࣾ಺ඪ४΁ʣ • ౰ॳ͸A/BςετͷઃఆͷΈ؅ཧɾ഑෍͢ΔCMSͩͬͨ • ࠷ۙɺϦΞϧλΠϜͰͷμογϡϘʔυػೳ΋αϙʔτ •
15. POSITIONS
16. ϓϩδΣΫτମ੍ʢؔ܎ऀҰཡʣ ࣄۀαΠυʹ اըɾ։ൃɾ෼ੳ౳ͷؔ ܎ऀ͕ଘࡏ Hadoop, Kafka, etc.ͷࣾ಺σ ʔλΠϯϑϥɺϛυϧ ΢ΣΞ։ൃ ߴ౓ͳσʔλ ෼ੳʹΑΔࣄۀ෦ଆͷҙࢥ ܾఆࢧԉ΍ɺϞχλϦϯά΍ ϨϙʔςΟϯά Data ͷऔΓճ͠ʹؔΘ Δ֤छͷ੔ཧ΍ௐ੔ ͳͲ 1
17. ༷ʑͳ໾ׂͷϝϯό͕࿈ܞ͠ɺϓϩμΫτͷ։ൃɾอकӡ༻Λ࣮ࢪ ֤ϙδγϣϯͷ໾ׂ ML Engineer ML Server-side Engineer ML Product Manager ML Project Manager ػցֶशͷΞϧΰϦζϜͷ ݕ౼΍બఆͱ࣮૷ MLΤϯδϯΛ૊ΈࠐΜͩγ εςϜ΍ϓϥοτϑΥʔϜɾ APIͳͲͷ։ൃɾอक MLΤϯδϯΛಈ͔ͨ͢Ίͷ ܭࢉػ؀ڥͷߏஙʙӡ༻ MLΛ༻͍ͨγεςϜ΍ϓϥ οτϑΥʔϜͷاը ։ൃ෺ʹؔ͢Δࣄۀ෦౳΁ ͷఏҊ΍ಋೖͷਪਐ MLΛ༻͍ͨγεςϜ΍ϓϥ οτϑΥʔϜͷ։ൃ؅ཧ ϦϦʔεɾಋೖޙͷMLαʔ Ϗεͷܧଓతվળͷਪਐ ※࣮຿Ͱ͸Ұ໊͕ෳ਺ϩʔϧͰͷۀ຿਱ߦΛߦ͏৔߹΋͋Γ·͢ʢຊਓͷదੑ΍ܦݧʹΑΔʣ
18. ʢJob DescriptionͷهࡌΑΓൈਮʣ ඞਢͷܦݧɾεΩϧ ML Engineer ML Server-side Engineer ML Product Manager ML Project Manager ػցֶशɺίϯϐϡʔλʔαΠ Τϯεɺ਺ֶͷઐ໳తͳ஌ࣝ ࣄۀ/ϏδωεΛཧղ্ͨ͠ Ͱɺ෼ੳɾఏҊ͕Ͱ͖Δ͜ͱ σʔλͷ୳ࡧɺಛ௃ྔͷม׵ɺ Ϟσϧͷಋग़ɺγεςϜͷ࣮ ૷ɺύϑΥʔϚϯεධՁͷҰ௨ ΓͷߦఔΛ TerabyteʙPetabyte ن໛ͷେن໛σʔλͰ࣮ࢪͰ͖ ΔεΩϧ ෼ࢄॲཧγεςϜʢHadoop, Spark, MPI, etc.ʣͷ஌ࣝɾܦݧ ίϯϐϡʔλʔαΠΤϯεʹؔ ͢Δશൠతͳ஌ࣝ ԿΒ͔ͷαʔϏεɾϓϩδΣΫ τʹ͓͚ΔAPI΍γεςϜͷ։ ൃɺ͓Αͼӡ༻ܦݧ ෼ࢄॲཧγεςϜʢHadoop, Spark, etc.ʣ্ͰͷσʔλՃ ޻ɺγεςϜͷ࣮૷ɺύϑΥʔ ϚϯεධՁͳͲͷߦఔΛ TerabyteʙPetabyteن໛ͷେن ໛σʔλͰ࣮ࢪͰ͖ΔεΩϧ ػցֶशΤϯδχΞͱڠಇͯ͠ ۀ຿Λ਱ߦ͢ΔͨΊͷɺػցֶ शʹؔ͢Δجຊత஌ࣝ ػցֶशٕज़Λ༻͍ͨαʔϏε ͷ։ൃɺ·ͨ͸։ൃ؅ཧͷܦݧ ࣄۀ/Ϗδωεɾػցֶशٕज़ Λཧղ্ͨ͠Ͱɺجૅతͳ෼ੳ ʙاըɾఏҊ͕Ͱ͖Δೳྗ αʔϏεͷ։ൃऀ΍Ϗδωε෦ ໳౳ͷؔ܎ऀͱ࿈ܞ͠ɺεϜʔ ζʹۀ຿ΛਐΊΔͨΊͷίϛϡ χέʔγϣϯೳྗ ίϯϐϡʔλαΠΤϯεઐ߈ and/or ιϑτ΢ΣΞ։ൃͷܦݧ ϓϩδΣΫτϚωʔδϟʔ΍ͦ Εʹ४ͣΔ৬຿ܦݧ αʔϏεͷ։ൃऀ΍Ϗδωε෦ ໳౳ͷؔ܎ऀͱ࿈ܞ͠ɺεϜʔ ζʹۀ຿ΛਐΊΔͨΊͷίϛϡ χέʔγϣϯೳྗ
19. TEAM MEMBERS' VOCICES
20. ΍Γ͕͍ɾ޲͍͍ͯΔਓ ML ENGINEERING AT LINE σʔλαΠζ͕େ͖͍ͷͰɺܭࢉ؀ڥߏஙʹ͓͍ͯάάͬͨ಺༰͕໾ ʹཱͨͣɺࣗ෼Ͱߟ͑ͯ࡞Βͳ͚Ε͹ͳΒͳ͍ɻͦͷ෼ɺࡋྔ͕େ͖ ͍ͷͰָ͍͠ ࣗ෼Ͱߟ͑ͨ΋ͷΛߏஙɺҡ࣋Ͱ͖Δɻࣾ಺ͷHadoop؀ڥͳͲ΋ ࢼߦࡨޡͰߏங͞Ε͍ͯΔɻҠߦఫୀͳͲ΋ଟ͍ͨΊɺؒҧͬͨ ΋ͷ΍΋͏࢖Θͳ͘ͳͬͨ΋ͷΛ࡞ͬͯ΋Ԝ·ͣʹ࡞Γ௚ͤΔ ๲େʹσʔλ͕͋ΔʢʮϞσϧߏஙΛؤுͬͯ΋ͦ΋ͦ΋σʔλ͕ গͳͯ͘μϝʯͱ͍͏͜ͱ͕গͳ͍ʣ ߴ͍ٕज़΍ࢹ࠲Λ࣋ͬͨϝϯόʔ͕ଟ͍தͰɺ͔ͳΓࣗ༝ʹ։ ൃ͕ߦ͑Δ ཧ૝ͱݱ࣮ͷόϥϯεΛऔΓͳ͕Βɺࣗ෼ͳΓͷࠜڌΛ࣋ͬͯ ։ൃ͕ߦ͑Δ ੹೚ײ͕͋Δਓʢಉ྅΍ࣾ಺ؔ܎ऀͱͷ৴པΛੵΈ্͍͛ͯ͘ ͜ͱͰɺେ͖ͳ࢓ࣄ͕Ͱ͖ΔΑ͏ʹͳ͍ͬͯ͘ʣ ઌਐతͳख๏Λҙཉతʹࢼͭͭ͠΋ɺैདྷͷख๏ͷྑ͞΋େࣄʹͰ ͖Δਓ ʢσʔλ͕๲େͩͬͨΓɺܭࢉ࣌ؒʹ੍໿͕͋ͬͨΓ͢Δ ͷͰɺ ࠷৽ͷख๏ʹͩ͜ΘΓ͗͢Δͱ͏·͍͔͘ͳ͍͜ͱ΋ʣ ෼ࢄॲཧج൫Λ༻͍ͨϨίϝϯυΤϯδϯͷઃܭɺ࣮૷ ԣஅతʹσʔλ׆༻͠ɺ਎ۙͳαʔϏεʹߩݙ͢Δ ࣗൃతʢSelf-motivatedʣͳਓʢৗʹ࢓ࣄ͕༩͑ΒΕΔΘ͚Ͱ͸ͳ ͍ɻࣗݾम࿅ΛߦͬͨΓɺࣗΒఏҊ͢Δ͜ͱ΋ඞཁʣ ίϛϡχέʔγϣϯʹରͯ͠લ޲͖ͳਓʢձࣾશମͱͯ͠ݟͯ΋ɺ ݞॻͰ࢓ࣄ͢Δਓ͸গͳ͍ɻ๬Ί͹ɺ୭ͱͰ΋࢓ࣄ͕Ͱ͖Δʣ ఏҊ͔Βɺ։ൃɺ෼ੳɺӡ༻ʢSREʣ·Ͱ͢΂ͯؔΘΔ
21. REFERENCE
22. • Building a smart recommender system across LINE services (LINE DEVELOPER DAY 2019) • https://logmi.jp/tech/articles/322247 • LINEॳͷΤϯδχΞϑΣϩʔฒ઒३͞Μ͸ɺͲΜͳ࢓ࣄΛ͍ͯ͠Δͷ͔ • https://engineering.linecorp.com/ja/blog/line-engineer-insights-vol-10-line-fellow/ • Machine Learning at LINE (LINE DEVELOPER DAY 2018ʣ • https://logmi.jp/tech/articles/320435 • LINE's AB Test Standardization Process with Our Own Toolset (LINE DEVELOPER DAY 2018) • https://www.slideshare.net/linecorp/lines-ab-test-standardization-with-our-own- toolset-124023353 ࢀߟࢿྉ