ペパボにおける研究所の役割 - 研究と開発の相乗効果 - / Think about research and development

2018.01.19 香川大学 SLP

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1. ݚڀͱ։ൃͷ૬৐ޮՌ ࡾ୐ ༔հ / Pepabo R&D Institute, GMO Pepabo, Inc. 2018.01.19 ߳઒େֶ SLP ϖύϘʹ͓͚Δݚڀॴͷ໾ׂ
2. ϓϦϯγύϧΤϯδχΞ ࡾ୐ ༔հ / @monochromegane 2 http://blog.monochromegane.com Yusuke Miyake ϖύϘݚڀॴ ݚڀһ
3. • େֶଔۀޙɺ஍ݩ෱ԬͷSIerۈ຿Λܦͯɺ2012೥ΑΓגࣜձࣾ paperboy&co.(ݱGMOϖύϘגࣜձࣾ)ʹۈ຿ɻ • ϜʔϜʔυϝΠϯɺminneͱ͍ͬͨαʔϏεͷӡ༻։ൃͷ๣Βɺϩά׆༻ج൫ BigfootͷߏஙʹऔΓ૊ΈɺαʔϏεΛಈతʹվળ͍ͯͨ͘͠Ίͷ࢓૊Έͮ͘ ΓͱػցֶशʹڵຯΛ࣋ͭɻ • ·ͨɺOSS׆ಈͱͯ͠ओʹGoݴޠʹΑΔπʔϧ։ൃΛߦ͍ͬͯΔɻ୅දϓϩ μΫτͱͯ͠the_platinum_searcherͳͲ͕͋Δɻ 3 Career
4. 4 Activity ♕ 3 GitHub Awards Go ranking in Japan :) http://git-awards.com/users?country=japan&language=go
5. 1. ϖύϘݚڀॴʹ͍ͭͯ 2. ݚڀࣄྫͷ঺հ 3. ։ൃऀࢹ఺͔Βݚڀऀࢹ఺΁ɻ͋Δ͍͸ͦͷཱ྆ɻ 5 ໨࣍
6. 1. ϖύϘݚڀॴʹ͍ͭͯ
7. ϖύϘݚڀॴͷϛογϣϯ
8. 8 ϖύϘݚڀॴ(ུশʮϖύݚʯ)͸ɺࣄۀΛࠩผԽ Ͱ͖Δٕज़Λ࡞Γग़ͨ͢ΊʹʮͳΊΒ͔ͳγες Ϝʯͱ͍͏ίϯηϓτͷԼͰݚڀ։ൃʹऔΓ૊Ή ૊৫Ͱ͢ɻ ❝ ϖύϘݚڀॴʹ͍ͭͯ http://rand.pepabo.com/
9. ͳͥݚڀ͔
10. • ݚڀ͸ɺࣄ࣮΍ཧ࿦Λ໌Β͔ʹ͢Δʹ͋ͨΓɺ৽نੑɺ༗ޮੑɺ৴པੑΛ٬؍ తʹࣔ͞ͳ͚Ε͹ͳΒͳ͍ • طଘख๏ͷௐࠪɺ뱌ɺ࿦จԽΛܦͯɺख๏΁ͷঢ՚ʹࢸΔ • ݚڀతΞϓϩʔνͷ൓෮ʹΑͬͯɺཧ࿦΍ख๏͕લਐ͢Δ • ٕज़͕ҰൠԽ͢Δ࣌୅Ͱ͸ɺݚڀΛ௨ͯ͋͠Δख๏ʹ͓͚Δ৽نੑΛݗҾͰ͖ ΔଘࡏͱͳΔ͜ͱ͕ɺࠩผԽͰ͖Δٕज़Λ࣋ͭ͜ͱʹͭͳ͕Δ 10 ͳͥݚڀ͔ ࠩผԽͰ͖Δٕज़Λ࡞Γग़ͨ͢Ίʹ͸ɺݚڀతΞϓϩʔν͕༗ޮ
11. ͳΊΒ͔ͳγεςϜ
12. 12 γεςϜͷ֤ཁૉ͕໌ࣔతͳૢ࡞Λܦͣʹಛ௃Λ ೝࣝ͠ɺͦͷಛ௃΍ؔ܎ੑʹج͖ͮɺͦͷ࣌ʑͷ ঢ়گʹԠͨ͡࠷దͳαʔϏεΛఏڙ͢Δ ❝ ͳΊΒ͔ͳγεςϜ http://rand.pepabo.com/
13. ϖύݚͱαʔϏεͷؔ܎
14. 14 ΞΧσϛοΫͳਫ४ʹ͓͚Δ৽نੑɾ༗ޮੑɾ৴ པੑΛ௥ٻ͢ΔݚڀΛߦ͏ͱͱ΋ʹɺݚڀ։ൃ͠ ٕͨज़Λ࣮ࡍͷγεςϜͱ࣮ͯ͠૷ɾఏڙ͢Δ͜ ͱΛ௨ͯ͠ɺࣄۀͷ੒௕ʹߩݙ͠·͢ɻ ❝ ϖύϘݚڀॴʹ͍ͭͯ http://rand.pepabo.com/
15. 15 ϖύݚͱαʔϏεͷؔ܎ ࣄۀΛࠩผԽ͢ΔͨΊʹ͸ɺݚڀॴͱαʔϏεͷ࿈ܞ͕ඞਢ ݚڀ ։ൃ ӡ༻ ՝୊ͷڞ༗ ݚڀʹΑΔղܾ ಋೖ࣌ͷΤϯδχΞؒ࿈ܞ ݚڀ։ൃ݁ՌΛଈ࣌αʔϏεʹಋೖ͢Δ࢓૊ΈͱɺಋೖޙͷϑΟʔυόοΫʹΑΔαΠΫϧͷߴ ଎ԽʹΑͬͯɺݚڀ։ൃͷߴ౓ԽͱࣄۀͷࠩผԽʹͭͳ͛Δ
16. 2. ݚڀ։ൃࣄྫͷ঺հ
17. ಛ௃நग़ثͷֶशͱߪങཤྺΛ ඞཁͱ͠ͳ͍ྨࣅը૾ʹΑΔ ؔ࿈঎඼ݕࡧγεςϜ
18. 18 ՝୊ͷڞ༗ େ͖ͳ୯ҐͰͷ՝୊ʢઓུʣͷڞ༗ • minneʹ͓͍ͯɺ࡞඼ͱͷग़ձ͍ͷ֬཰Λ্͛Δ͜ͱ͕ઓུͷͻͱͭͱ্ͯ͠ ͛ΒΕ͍ͯΔɻݱࡏɺminneʹ͸਺ඦສ఺ͷ࡞඼͕ొ࿥͞Ε͓ͯΓɺαʔϏε ར༻ऀͷ௨ৗͷߦಈͰ͸શͯͷ࡞඼ΛݟͯճΔ͜ͱ͸ࠔ೉Ͱ͋Δɻ • ඞવతʹαʔϏεར༻ऀ͕ߪೖ͍ͨ͠ͱࢥ͏࡞඼ͱग़ձ͏֬཰͸௿Լ͖ͯͯ͠ ͓Γɺ͜ͷ֬཰Λ্͛Δ͜ͱ͕ɺ͓ങ͍෺ମݧͷ࠷େԽͷͨΊʹٻΊΒΕ͍ͯ Δɻ
19. • ճ༡ͷಋઢΛ૿΍͢ඞཁ͕͋ΔɻAmazon΍ָఱͱ͍ͬͨECαΠτͰ͸ؔ࿈ ࡞඼Λఏࣔ͢Δ͜ͱͰ঎඼ͷݕ౼Λܧଓͤ͞Δճ༡͕͋ΓɺͦͷͨΊʹ͸ͳΜ Β͔ͷ؍఺Ͱؔ࿈͍ͯ͠Δ͜ͱΛγεςϜతʹѻ͑Δঢ়ଶʹ͢Δඞཁ͕͋Δ 19 ண؟఺ͱํࣜ • ௨ৗɺDB্ʹؚ·ΕΔ৘ใʢߏ଄Խͨ͠৘ใʣʹΑͬͯಉҰࢹͰ͖Δ΋ͷ ʢminneͰݴ͑͹ಉ͡ΧςΰϦɺಉ͡৭ʣͳͲΛ༻͍Δ͕ɺ͜Ε·Ͱʹͳ͍ؔ ࿈࡞඼ͷಋઢΛ૿΍ͨ͢Ίɺ·ͩߏ଄Խ͞Ε͍ͯͳ͍৘ใΛminne಺ͰऔΓѻ ͑ΔΑ͏ʹ͍ͯ͘͠ɻ
20. • ߪങཤྺ౳ͷ৘ใ͕ෆཁͰ෼ྨ༻ͷ௥Ճ৘ใͱͯ͠Ͱ͸ͳ͘ɺৗʹઃఆ͞ΕΔ ঎඼ը૾Λର৅ • ಋೖઌͷECαΠτͷ঎඼ʹґଘ͠ͳֶ͍शෆཁͰ൚༻తͳֶशࡁΈωοτ ϫʔΫΛಛ௃நग़ثͱͯ͠࠾༻ • ಛ௃நग़ث͔ΒಘΒΕͨಛ௃ྔΛ΋ͱʹۙࣅۙ๣୳ࡧʹΑΓྨࣅը૾Λݕࡧ 20 ఏҊख๏
21. ಛ௃நग़ث
22. • m࣍ݩͷϕΫτϧxΛn࣍ݩͷϕΫτϧy΁ࣸ૾͢Δؔ਺fΛਪఆ͢Δֶशख๏ 22 χϡʔϥϧωοτϫʔΫ: Ϟσϧ x1 x2 xm b W y1 y2 yn 1 2 o1 = σ( m ∑ i=0 w1i xi + b1 ) h 3 o = σ (Wx + b) w11 w12 . . w1m w21 w22 . . w2m . . wh1 wh2 . . whm x1 x2 . . xm + b1 b2 . . bh x f y h wh1 wh2 x1 x2 ೖྗ͝ͱʹॏΈXΛ৐ࢉ
    
    όΠΞε
    ͨ͠΋ͷΛ׆ੑԽؔ਺ʹ௨͢ શϊʔυʹର͢Δॲཧ͸ߦྻͱͯ͠ૢ࡞Ͱ͖Δ
23. 23 χϡʔϥϧωοτϫʔΫ: ֶश x1 x2 xm y1 y2 yn x f y • m࣍ݩͷϕΫτϧxΛn࣍ݩͷϕΫτϧy΁ࣸ૾͢Δؔ਺fΛਪఆ͢Δֶशख๏ t tn t2 t1 W(1) W(2) b(1) b(2) ✓(i+1) = ✓(i) ↵rEk E = 1 2 X n (yn tn)2 E = X n tn ln yn rEk = ( @Ek @✓1 , .. @Ek @✓V ) ग़ྗͱਖ਼ղͷޡࠩΛද͢ଛࣦؔ਺Λఆٛ͢Δɻ
    ೋ৐࿨ޡࠩʢࠨʣɺަࠩΤϯτϩϐʔޡࠩʢӈʣ ଛࣦؔ਺ʹର͢Δ֤ύϥϝλͷภඍ෼ͷ஋ͷू·Γʢޯ഑ʣΛ༻ ͍ͯଛࣦؔ਺ͷ஋͕খ͘͞ͳΔΑ͏ʹύϥϝλΛߋ৽͢Δ͜ͱΛ ޯ഑߱Լ๏ͱݺͿɻಛʹҰ෦ͷ݁ՌͷΈΛར༻͢Δ֬཰తޯ഑߱ Լ๏͕Α͘༻͍ΒΕΔɻ
    Αͬͯޯ഑͕ফ͑ͳ͍Α͏ͳͳΊΒ͔ͳ׆ੑԽؔ਺͕޷·ΕΔɻ
    ·ͨɺ֤ύϥϝλͷภඍ෼ΛݸผʹٻΊΔͷͰ͸ͳ͘ɺχϡʔϥ ϧωοτϫʔΫΛදؔ͢਺GΛ߹੒ؔ਺ͱݟཱͯΔ͜ͱͰɺ্ྲྀ ͷޡࠩͱࣗ਎ͷೖྗ஋ͷΈΛ࢖ͬͯܭࢉྔΛ཈͑ͳ͕Βޮ཰తʹ ޯ഑ΛٻΊΔख๏Λޡࠩٯ఻೻๏ͱݺͿɻ
24. χϡʔϥϧωοτϫʔΫ: ֶश 24 • ࢀߟ (PʹΑΔޯ഑߱Լ๏
    
    ཧ࿦ͱ࣮ફ
    
    IUUQTTQFBLFSEFDLDPNNPOPDISPNFHBOFHSBEJFOUEFTDFOUJOHPMBOH θϩ͔Β࡞Δ%FFQ
    -FBSOJOH
25. ৞ΈࠐΈχϡʔϥϧωοτϫʔΫ 25                           ੵ࿨ԋࢉ ৞ΈࠐΈ                  ࠷େ஋ ೖྗσʔλʹରͯ͠ੵ࿨ԋࢉΛߦ͏ϑΟϧλΛద༻͠ɺ ಛ௃Ϛοϓͱͯ͠ग़ྗ͢Δɻۭؒ৘ใΛߟྀͰ͖Δɻ ࠷దͳϑΟϧλͷॏΈ͸ֶशʹΑͬͯ֫ಘ͢Δɻ • ۭؒత৘ใΛߟྀͨ͠χϡʔϥϧωοτϫʔΫ ϓʔϦϯά ೖྗσʔλʹରۭͯؒ͠ͷू໿Λߦ͏ϑΟϧλΛద༻ ͢Δɻ࠷େ஋΍ฏۉ஋Λར༻͢ΔͷͰֶश͕ෆཁɻ
    ࡉ͔ͳҐஔมԽʹର͢Δؤڧੑʹߩݙ͢Δɻ ಛ௃நग़ث ࣝผث
27. ಋೖ
28. ಛ௃ྔม׵ 28 Service Object Storage GCP image to data data to feature vectorizer by Inception-v3 Annoy Workers • ͋Δ࣌఺·Ͱͷ࡞඼ը૾ҰཡΛಛ௃ྔʹม׵͢Δ • ม׵ͨ͠ಛ௃ྔҰཡΛۙࣅۙ๣୳ࡧσʔλϕʔεʹ౤ೖ͢Δ
29. • ۙࣅۙ๣୳ࡧσʔλϕʔεΛmruby-annoy + ngx_mrubyʹͯAPIԽ • ࡞඼ৄࡉʹྨࣅը૾Λ༻͍ͨؔ࿈࡞඼Λදࣔ͢Δ 29 ྨࣅը૾ݕࡧ Nyah mruby-annoy on ngx_mruby products#show product_id nearest products CTR Analytics NNS ˞ۙࣅۙ๣୳ࡧ࣌ʹେ෦෼ͷΠϯσο Ϋε΁ͷΞΫηε͕ൃੜ͢ΔͨΊ࣮༻ తͳ଎౓ΛಘΔͨΊʹ͸σʔλϕʔε ϑΝΠϧ͕શͯϖʔδΩϟογϡʹࡌ ΔαΠζͷϝϞϦ͕ඞཁ
30. ྨࣅը૾ʹΑΔؔ࿈࡞඼ݕࡧ 30
31. ྨࣅը૾ʹΑΔؔ࿈࡞඼ݕࡧ 31
32. ΫϦοΫ཰ͱίϯόʔδϣϯ཰ 32 طଘ ఏҊ $53   $73   ˞ఏҊख๏ʹΑΔબఆ͕ߦ͑ͳ͍৔߹ʹαʔϏεͷ ػձଛࣦΛආ͚ΔͨΊطଘख๏ʹΑΔબఆΛߦͬͯ ͍ΔͨΊݕূظؒதͷ֤ख๏ͷදׂࣔ߹͸طଘ ɺఏҊͰ͋ͬͨ
    ˞ίϯόʔδϣϯ཰͸ΫϦοΫ਺ʹର͢Δߪೖ਺͔ ΒٻΊͨ طଘ ఏҊ ૉࡐɾࡐྉγΣϧ   ૉࡐɾࡐྉϦϘϯɾςʔϓ   ૉࡐɾࡐྉϘλϯ   ͵͍͙ΔΈɾਓܗ͋Έ͙ΔΈ   χοτɾฤΈ෺ηʔλʔɾΧʔσΟΨϯ   ఏҊख๏͕༗ޮͰ͋ͬͨΧςΰϦ
33. ݚڀใࠂΠϯλʔωοτͱӡ༻ٕज़ʢIOTʣ 33 ࡾ୐ ༔հ, দຊ ྄հ, ྗ෢ ݈࣍, ܀ྛ ݈ଠ࿠, ಛ௃நग़ثͷֶ शͱߪങཤྺΛඞཁͱ͠ͳ͍ྨࣅը૾ʹΑΔؔ࿈঎඼ݕࡧγ εςϜ, ݚڀใࠂΠϯλʔωοτͱӡ༻ٕज़ʢIOTʣ, Vol.2017-IOT-37(4), pp.1-8, May 2017 http://id.nii.ac.jp/1001/00178892/
34. ϑΟʔυόοΫ
35. Gannoy Approximate nearest neighbor search server and dynamic index written in Golang. https://github.com/monochromegane/gannoy
36. GannoyʹΑΔಈతΠϯσοΫεߋ৽+ྨࣅը૾ݕࡧ 36 Features Similar items Gannoy [2048]float64 query by http find similar features mapping similar features to items response Deep CNN index Features [2048]float64 Deep CNN register by http
37. ΞΫηεස౓༧ଌʹجͮ͘ Ծ૝αʔόͷܭըతΦʔτεέʔϦϯά
38. 38 ՝୊ͷڞ༗
39. • ैྔ՝ۚͷԾ૝αʔόӡ༻ʹ͓͍ͯ࠷దͳϦιʔεधཁͷ༧ଌ͸ίετΧοτʹͭͳ͕Δ • WebαʔϏεͷϦιʔεधཁ͸ϦΫΤετॲཧ݅਺ɺͭ·ΓΞΫηε਺ͱ૬͕ؔ͋Δ͸ͣ • Ϧιʔεͷ૿ݮʹ΋͋Δఔ౓ͷ͕͔͔࣌ؒΔͨΊɺϦΞϧλΠϜͰ͸ͳ͘ҰఆִؒͰͷΞ Ϋηε਺༧ଌͰे෼ͱߟ͑Δ 39 ண؟఺ͱํࣜ ΞΫηε਺Λ༧ଌͰ͖ΔΑ͏ʹͳΕ ͹ɺϐʔΫλΠϜʹ͋Θͤͨ୆਺ݟੵ ΋Γ͔Β࣌ؒ͝ͱͷ࠷దʢͱࢥΘΕ Δʣ୆਺ݟੵ΋Γ͕ՄೳʹͳΔ
40. • WebαʔϏεશମͰҰఆ࣌ؒʹॲཧͨ͠ΞΫηεස౓Ͱ͋ΔεϧʔϓοτΛ ࢦඪͱ͠ɺӡ༻্ɺܦݧతʹ೺Ѳ͞Ε͍ͯΔ҆ఆͯ͠ӡ༻Մೳͳ஋Λ໨ࢦ͢ • աڈͷΞΫηεස౓܏޲ͱෆఆظͳมಈཁҼ͔Β༧ଌϞσϧΛಋ͘ • ༧ଌతͳߏ੒มߋΛ՝ۚ୯ҐͰ͋Δ1࣌ؒΛ୯Ґʹߦ͏ 40 ఏҊख๏
41. 41 ఏҊख๏
42. 42 ΞΫηεස౓༧ଌϞσϧ ΞΫηεස౓༧ଌϞσϧ ֶशσʔλ͸Ϋϥ΢υαʔϏεͷඪ४՝ ۚ୯ҐͰ͋Δ࣌ؒΛཻ౓ͱ͢Δ 8FCαʔϏεͷ࠷୹ఆৗੑΛ֬ೝͰ͖Δ࣌ؒ෼ͷσʔλ Λೖྗͱ͠ɺ࣍ͷ࣌ؒ෼ͷΞΫηεස౓༧ଌΛग़ྗͱ͢Δ
    ˞࣌ؒޙҎ߱͸༧ଌ෼ΛؚΊͨظΛೖྗͱ͢Δ
43. ࠶ؼܕχϡʔϥϧωοτϫʔΫ 43 • ܥྻ৘ใΛѻ͏χϡʔϥϧωοτϫʔΫ f W(1) W(2) W x1 x2 xt y1 y2 yt z z1 z2 zt x y zt 1 zt xt ҎલͷೖྗʹӨڹΛड͚ΔΑ͏ͳॱংʹҙຯ͕͋Δܥྻσʔλ ΛऔΓѻ͏ͨΊɺதؒ૚ͷग़ྗΛࣗ਎ʹ໭͢ɻಈతʹ૚਺͕ม ΘΔωοτϫʔΫͱଊ͑ΒΕΔɻ ๨٫ήʔτ ग़ྗήʔτ ೖྗήʔτ ૚਺ͷਂ͍ωοτϫʔΫͱͯ͠ల։͞ ΕΔܥྻσʔλΛѻ͏ͨΊɺ3//ͷ ϊʔυΛϝϞϦϢχοτʹஔ͖׵͑ͨ -45.͕ఏҊ͞Εͨɻ
    ೖग़ྗήʔτʹΑΓબ୒తʹσʔλΛ ௨աɺ๨٫ήʔτʹΑΓෆཁͳ৘ใΛ Ϧηοτ͢Δ͜ͱͰঢ়گมԽΛଊ͑ͳ ͕Β௕ظґଘͷܥྻσʔλΛѻ͑Δɻ
44. 44 Ծ૝αʔό୆਺ࢉग़ • ༧ଌͨ͠ΞΫηεස౓ΛجʹɺWebαʔϏεΛ҆ఆͯ͠ӡ༻ Ͱ͖Δ໨҆ͱͳΔεϧʔϓοτ஋Λ֬อͰ͖Δ୆਺ΛٻΊΔ ༧ଌΞΫηεස౓ʹର͠εϧʔϓοτΛ
    ֬อͰ͖Δ୆਺Λࢉग़͢Δ
    
    
    5
    <ΞΫηεස౓෼>
    
    
    1
    <༧ଌΞΫηεස౓࣌>
    
    
    -
    αʔό୆਺Լݶ஋
45. ಋೖ
46. • ࠓճͷධՁͰ͸ɺର৅ͷ WebαʔϏεʹ͓͍ͯཌ೔ ͕ฏ೔ͷ৔߹ɺ໷ؒʹΞΫ ηεස౓͕૿Ճ͢Δͱ͍͏ ܦݧଇΛཁҼͱͯ͠Ճ͑ͨ 46 ඇఆৗͷཁҼͷՃຯʹΑΔ༧ଌਫ਼౓ͷධՁ
47. 47 ඇఆৗͷཁҼͷՃຯʹΑΔ༧ଌਫ਼౓ͷධՁ ໷ؒʹීஈͱҟͳΔ܏޲ͱͳΔಛੑΛଊ ͑ͨ༧ଌ͕ߦΘΕ͍ͯΔɻ
48. 48 ܭըతΦʔτεέʔϦϯάͷධՁ Ծ૝αʔό୆਺ͷਪҠ
    ೔͋ͨΓͷαʔό૯ىಈ࣌ؒ͸"܈ ը૾্ Ͱ͔࣌ؒΒ࣌ؒʹɺ#܈ ը૾Լ Ͱ ͸ ͔࣌ؒΒ࣌ؒʹ࡟ݮ
    ˞"܈ͷ࣌͸ࢉग़୆਺͕୆਺Լݶ஋ΛԼ ճͬͨͨΊɺ୆਺ͷมಈ͸ݟΒΕͳ͍
49. 49 ܭըతΦʔτεέʔϦϯάͷධՁ ΞΫηεස౓ͷਪҠ
    ೔͋ͨΓΞΫηεස౓ͷඪ४ภࠩ͸"܈ ը ૾্ Ͱ໿͔Β໿ʹɺ#܈ ը ૾Լ Ͱ͸໿͔Β໿ʹมԽɻ ख๏ద༻ޙʹεϧʔϓοτ͕҆ఆ͍ͯ͠Δ ͜ͱ͕Θ͔Δɻ
    ˞"܈ͷ૿Ճ͸Լݶ୆਺ӡ༻ͱͳͬͨ࣌ؒଳ ͷ୆͋ͨΓͷεϧʔϓοτ஋૿ՃʹΑΔ΋ ͷͱߟ͑ΒΕΔ
50. ݚڀใࠂΠϯλʔωοτͱӡ༻ٕज़ʢIOTʣ 50 ࡾ୐ ༔հ, দຊ ྄հ, ྗ෢ ݈࣍, ܀ྛ ݈ଠ࿠, ΞΫηεස౓༧ ଌʹجͮ͘Ծ૝αʔόͷܭըతΦʔτεέʔϦϯά, ݚڀใࠂ Πϯλʔωοτͱӡ༻ٕज़ʢIOTʣ, Vol.2017-IOT-38(13), pp.1-8, June 2017 http://id.nii.ac.jp/1001/00182375/
51. ݚڀॴͱαʔϏεͷ࿈ܞ
52. 1. ج൫ԽɺAPIԽʹΑΔݚڀ؀ڥͱαʔϏεͷγʔϜϨεͳ࿈ܞ 2. ίʔυ؅ཧɺόʔδϣϯ؅ཧʹΑΔݚڀͱӡ༻ͷฒߦ 52 ݚڀ݁ՌͷαʔϏεಋೖ ݚڀ݁ՌͷαʔϏεಋೖ͸ଈ͔࣌ͭશࣾల։Ͱ͖Δ͜ͱ͕๬·͍͠
53. ػցֶशج൫
54. 1. ϩά΍DBͳͲͷαʔϏεࢿ࢈ͱ࿈ܞͰ͖Δ 2. ൺֱత༰қʹϞσϧͷߏஙͱࢼߦ͕ߦ͑Δ 3. ֶश݁ՌΛར༻͢ΔͨΊͷखஈͱͯ͠APIΛఏڙ͢Δ 1. ֶश݁ՌͷϩʔΧϧར༻͕Ͱ͖Δͱͳ͓Α͍ 4. ্هͷ࢓૊Έ͕εέʔϥϒϧͰ͋Δ͜ͱ 54 ػցֶशج൫ʹٻΊΒΕΔ΋ͷ
55. 1. ೖग़ྗ͕Cloud Storageܦ༝ 2. ܇࿅ϓϩάϥϜͱͯ͠TensorFlowΛ࠾༻ 3. ΦϯϥΠϯ༧ଌαʔϏεʹΑΓϞσϧͷAPIԽ 1. ֶश݁Ռ͸Cloud StorageʹอଘɺϩʔΧϧͰͷར༻΋ 4. ෼ࢄܕͷτϨʔχϯάΠϯϑϥͱෛՙ෼ࢄαʔϏεͱͷ࿈ܞ 55 Google Cloud ML EngineͰߟ͑Δ ※ ݕ౼ʹؔ͢Δৄࡉ: 
 http://rand.pepabo.com/article/2017/01/18/pepabo-ml-platform-and-workflow/
56. StarChart StarChart is a tool to manage Google Cloud Machine Learning training programs and model versions https://github.com/monochromegane/starchart
57. • όʔδϣϯ؅ཧͷ੾ସʹ͓͚Δ൑அج४ͱͳΔ܇࿅ϓϩάϥϜɺύϥϝλɺδϣ ϒ৘ใ·ͰؚΊͯίʔυͰ؅ཧ • ֶश࣌ͷδϣϒID΍Cloud Storageͷύεɺόʔδϣϯʹඥͮ͘ύϥϝλ৘ใ ͷऔಘʹ·ͭΘΔCloud MLͷࡉ͔ͳ࢖͍উख΋վળ 57 StarChart
58. ϩάج൫
59. ϩάج൫ 59 IDFA/GAID UID rack-bigfoot Service Request Activity log Services DB Attribute Big Cube Cube https://icons8.com BI Recommendation Bandit algorithm Re-marketing Feedback Name identification Cookie Sync
60. SBDLCJHGPPU w3BJMTΞϓϦέʔγϣϯͱ'MVFOUEΛͭͳ͙3BDLϛυϧ΢ΣΞ
    w#JHGPPUʹඞཁͳڞ௨ύϥϝλΛϦΫΤετɾϨεϙϯεϔομ͔Βऔಘ
    wαʔϏεݻ༗ͷύϥϝλΛ෇༩͢Δ͜ͱ΋Մೳ Rails.application.config.app_middleware.insert_after ActionDispatch::Callbacks, Rack::Bigfoot do |config| config.service = 'minne' config.environment = Rails.env config.enable_fluent = Rails.env.production? || Rails.env.staging? config.ignore_path_patterns << %r(\A/healthcheck) config.headers << 'HTTP_X_CLIENT_VERSION' end
61. 5SFBTVSF
    %BUB wΫϥ΢υܕσʔλϚωδϝϯταʔϏε
    wIUUQTXXXUSFBTVSFEBUBDPN
    wେ༰ྔͷϩάอଘɺ෼ࢄॲཧʹΑΔߴ଎ͳϩάૢ࡞ log Plasma DB HiveQL export SQL aggregate Data Tanks
62. ϫʔΫϑϩʔ w5SFBTVSF%BUBͷεέδϡʔϧΫΤϦΛར༻
    wΫΤϦͷίʔυ؅ཧ༻ʹ
    1FOEVMVNΛ։ൃ
    wIUUQTHJUIVCDPNNPOPDISPNFHBOFQFOEVMVN
    w%4-ʹΑͬͯεέδϡʔϧΫΤϦΛهड़͠ɺίʔυ؅ཧ Scheduled queries Queries on GitHub Apply Pendulum
63. 3. ։ൃऀࢹ఺͔Βݚڀऀࢹ఺΁ɻ ͋Δ͍͸ͦͷཱ྆ɻ
64. ݚڀͬͯͳΜͩ
65. • ։ൃऀͱͯ͠ͷ೰Έ • ط஌ͷػցֶशख๏ΛαʔϏε՝୊ʹద༻͢ΔΞϓϩʔν • ৽نੑΛࣔ͠ʹ͍͘ͷͰ͸ͳ͍͔ • ط஌ͷػցֶशख๏ͷධՁͱͳͬͯ͠·͏ͷͰ͸ͳ͍͔ 65 ݚڀͬͯͳΜͩ
66. • ݚڀͱ͸ɺ໨తͰ΋खஈͰ΋ͳ͘ʮաఔʯͰ͸ͳ͍͔ 66 ݚڀͬͯͳΜͩ ͋Δ΂͖ੈքʹ޲͚ͯɺ٬؍తʹ࣮֬ʹҰาਐΜͩূ͕ ݚڀใࠂͰ͋Γɺ࿦จͰ͋Γɺ੒ՌͰ͋Δ • ͋Δ΂͖ੈքͷఆٛ • ͋Δ΂͖ੈքΛఆٛ͢Δɺ·ͨ͸ͦ͜ʹࢸΔख๏Λ࣮֬ʹਐΊΔͨΊͷ
 ઌߦݚڀͷཧղ • ͨΏ·͵ࢥߟʹΑΔ஍ಓͳҰาͮͭͷલਐ
67. ྨࣅը૾ݕࡧɺ࠶ߟ
68. • ৘ใ୳ࡧϓϩηε͸γεςϜଆͷ ٕज़ৄࡉ͚ͩͰ͸ͳ͘ɺ͜ͷϓϩ ηεΛۦಈ͢ΔओମͰ͋Δར༻ऀ ͕ଘࡏ͢Δ • ར༻ऀαΠυΛத৺ͱͨ͠৘ใ୳ ࡧϓϩηεϞσϧͷઌߦݚڀʹΑ Γɺ৘ใཁٻ͸ɺ༷ʑͳจ຺ʹΑ ΓมԽ͍ͯ͘͜͠ͱ͕Θ͔͍ͬͯ Δ 68 ͳΊΒ͔ͳϚονϯάʹ޲͚ͯ ৘ใཁٻΛऔΓרٕ͘ज़ͱϓϩηεϞσϧؔ࿈ਤ จ຺ʹΑΓ࠷దͳԠ౴͕มԽ͢ΔͷͰ͋Ε͹ɺ
    ٕज़ؒͰͷ༏ҐੑධՁͰ͸ͳ͘ɺ
    ঢ়گʹԠͨ͡બ୒͕ྑ͍ͷͰ͸ͳ͍͔
69. • ར༻ऀͷऔΓ͏Δߦಈ͕จ຺ʹΑΓมԽ͢ΔͱԾఆ্ͨ͠Ͱɺਫ਼៛ͳจ຺ͷ೺ Ѳฒͼʹ՝୊ઃఆͷ໌֬ԽࢧԉΛ௨ͯ͠ɺ௚ײతͳ໰͍߹ΘͤʹԠ౴Ͱ͖Δ࢓ ૊Έ 69 ͳΊΒ͔ͳϚονϯάʹ޲͚ͯ 1. จ຺ʹΑΓԠ౴ͷධՁ͕มΘΔ͜ͱͷ֬ೝ 2. ར༻ऀͷจ຺ͷύλʔϯ෼ྨͱࣝผख๏ͷཱ֬ 3. ඞཁʹԠͯ͡௚ײతͳ໰͍߹ΘͤʹԠ౴Ͱ͖Δݕࡧ৘ใٕज़ͷಋೖ 4. ՝୊ઃఆͷ໌֬Խࢧԉͷݕ౼
70. ݚڀͱ։ൃͷཱ྆
71. • ͋Δ΂͖ੈքʹ޲͚ͯ٬؍తɺ࣮֬ʹาΜͰ͍ͨ͘ΊͷݚڀऀεΩϧ • ՝୊Λղܾ͠ɺαʔϏεʹద༻͢ΔͨΊͷ։ൃऀεΩϧ • ͜ΕΒͷ૬৐ޮՌ͕ɺࣄۀΛࠩผԽ͢ΔͨΊͷٕज़Λ࡞Γग़͠ɺࣄۀͷ੒௕ʹ ߩݙ͢Δʂ 71 ݚڀͱ։ൃͷཱ྆
72. ݚڀһɺੵۃతʹืूதʂ http://rand.pepabo.com/
73. • ϖύϘݚڀॴ • http://rand.pepabo.com/ • ಛ௃நग़ثͷֶशͱߪങཤྺΛඞཁͱ͠ͳ͍ྨࣅը૾ʹΑΔؔ࿈঎඼ݕࡧγεςϜ • http://rand.pepabo.com/article/2017/06/19/iot37-miyakey/ • ΞΫηεස౓༧ଌʹجͮ͘Ծ૝αʔόͷܭըతΦʔτεέʔϦϯά • http://rand.pepabo.com/article/2017/06/28/iot38-miyakey/ • αʔϏεʹدΓఴ͏ϩάج൫ • https://speakerdeck.com/monochromegane/pepabo-log-infrastructure-bigfoot • Google Cloud ML Λ༻͍ͨػցֶशج൫ͷߏஙͱӡ༻ • https://speakerdeck.com/monochromegane/pepabo-ml-infrastructure-starchart 73 ࢀߟ