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機械学習基盤の本番運用とその取り組み / ML platform in production

Kohei Ota
December 08, 2019

機械学習基盤の本番運用とその取り組み / ML platform in production

Kohei Ota

December 08, 2019
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  1. ػցֶशج൫ͷຊ൪ӡ༻ͱ
    ͦͷऔΓ૊Έ
    1SFTFOUFECZ,PIFJ0UB !JOEVDUPS

    +VMZ5FDI'FTUB

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  2. ࣗݾ঺հ
    w ଠాߤฏ !@JOEVDUPS@ !JOEVDUPS

    w %PDLFS.FFUVQ5PLZP0SHBOJ[FS
    w *OGSBTUSVDUVSF&OHJOFFS .-0QT5FBN!;0;05FDIOPMPHJFT *OD

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  3. ;0;0508/ͷը૾ݕࡧػೳʹΈΔ
    ,VCFSOFUFTΛ࢖ͬͨػցֶशج൫ӡ༻ͷཪଆ
    $MPVE/BUJWF%BZT,BOTBJ

    $PQZSJHIU˜;0;05FDIOPMPHJFT *OD
    גࣜձࣾ;0;0ςΫϊϩδʔζ
    ։ൃ෦ .-0QTνʔϜ
    ଠా ߤฏ

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  4. ;0;0508/ͷը૾ݕࡧػೳʹΈΔ
    ,VCFSOFUFTΛ࢖ͬͨػցֶशج൫ӡ༻ͷཪଆ
    $MPVE/BUJWF%BZT,BOTBJ

    $PQZSJHIU˜;0;05FDIOPMPHJFT *OD
    גࣜձࣾ;0;0ςΫϊϩδʔζ
    ։ൃ෦ .-0QTνʔϜ
    ଠా ߤฏ
    ৄ͘͠͸4QFBLFSEFDLͷεϥΠυͰʂ

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  5. ࠓ೔ͷλʔήοτ
    w Πϯϑϥ%FW0QT43&తͳ͜ͱΛ΍͍ͬͯΔਓ
    w ػցֶशʹ͍ͭͯ͋·Γྑ͘෼͔͍ͬͯͳ͍ਓ ׬શʹཧղ͍ͯ͠Δਓ

    w ಥવৼΒΕΔṖͷλεΫʹରԠͰ͖Δ͔Θ͔Βͳ͍ਓ

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  6. ͋Δ೔͜Μͳ͜ͱΛݴΘΕΔͱ
    ߟ͑ͯΈ͍ͯͩ͘͞

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  7. ܦӦऀʮ͜Ε͔Β͸"*ͩʂʂʯ

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  8. "*ʁػցֶशʁͲ͏ҧ͏ͷʁ

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  9. ػցֶशͬͯͳʹʁʁ

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  10. ػցֶशʁʁ
    w σʔλΛೖྗɺ͋Δ΂͖݁ՌΛग़ྗͱͯ͠
    w ෳ਺ͷσʔλ͔Βڞ௨ͷϧʔϧ΍ಛ௃ɺ൑அج४Λநग़
    w ໌ࣔతʹϓϩάϥϜϩδοΫΛॻ͔ͣʹ݁ՌΛग़ͨ͢Ίͷ࢓૊Έ

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  11. ػցֶशʁʁ
    w σʔλΛೖྗɺ͋Δ΂͖݁ՌΛग़ྗͱͯ͠
    w ෳ਺ͷσʔλ͔Βڞ௨ͷϧʔϧ΍ಛ௃ɺ൑அج४Λநग़
    w ໌ࣔతʹϓϩάϥϜϩδοΫΛॻ͔ͣʹ݁ՌΛग़ͨ͢Ίͷ࢓૊Έ
    ϩδοΫͷந৅Խ

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  12. ͳͥػցֶश͕ٻΊΒΕΔͷ͔
    w ϞόΠϧ΍Πϯλʔωοτ͕ීٴ͖ͬͨ͠ࠓɺ෺ࣄͷঢ়گ͸ٸܹʹมΘΔ
    w ͦͷมԽΛϓϩάϥϜͰද͢ͷ͸େม
    w *G΍GPSͰઃఆͨ͠ύϥϝʔλʔΛॻ͖׵͑Δ͚ͩͷվमΛՃ͑Δͷ͸

    ೉͍͠͠ɺޮՌଌఆ΋େมʢ͍ΘΏΔzਓ޻ແೳzͱ͍͏ݺ͹ΕΔ΍ͭʣ
    w ͜Ε·Ͱͷ܏޲͔Β࣍ͷ݁ՌΛ༧ଌ͢Δࣄ͕Ͱ͖Δശ͕͋Ε͹

    ຊ࣭తʹ͸αʔϏεͷϩδοΫ͸໰୊Ͱ͸ͳ͍͸ͣ

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  13. ͳͥػցֶश͕ٻΊΒΕΔͷ͔
    w ϞόΠϧ΍Πϯλʔωοτ͕ීٴ͖ͬͨ͠ࠓɺ෺ࣄͷঢ়گ͸ٸܹʹมΘΔ
    w ͦͷมԽΛϓϩάϥϜͰද͢ͷ͸େม
    w *G΍GPSͰઃఆͨ͠ύϥϝʔλʔΛॻ͖׵͑Δ͚ͩͷվमΛՃ͑Δͷ͸

    ೉͍͠͠ɺޮՌଌఆ΋େมʢ͍ΘΏΔzਓ޻ແೳzͱ͍͏ݺ͹ΕΔ΍ͭʣ
    w ͜Ε·Ͱͷ܏޲͔Β࣍ͷ݁ՌΛ༧ଌ͢Δࣄ͕Ͱ͖Δശ͕͋Ε͹

    ຊ࣭తʹ͸αʔϏεͷϩδοΫ͸໰୊Ͱ͸ͳ͍͸ͣ
    ػցֶशʹΑͬͯಘΒΕͨϞσϧ ϒϥοΫϘοΫε
    Λ
    ೖΕସ͑Δ͚ͩͰΞϓϦέʔγϣϯ͕ੈͷதͷ

    χʔζʹରԠͰ͖ΔͷͰ͸ͳ͍͔ʂͱ͍͏ൃ૝

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  14. ػցֶशͷԠ༻ྫ
    w ݕࡧΤϯδϯͷϦίϝϯσʔγϣϯ
    w ؞΍޻৔ʹ͓͚Δෆྑ඼ͳͲͷҟৗݕ஌
    w Ի੠ೝࣝɺը૾ೝࣝ
    w גՁ༧ଌ

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  15. ػցֶशͷԠ༻ྫ
    w ݕࡧΤϯδϯͷϦίϝϯσʔγϣϯ
    w ؞΍޻৔ʹ͓͚Δෆྑ඼ͳͲͷҟৗݕ஌
    w Ի੠ೝࣝɺը૾ೝࣝ
    w גՁ༧ଌ
    ϦΞϧλΠϜͰมԽ͢Δ΋ͷ΍

    ਓؒͷखΛհ͢Δͱେมͳ΋ͷʹରͯ͠ಛʹ༗ޮ

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  16. ػցֶशͷར༻Πϝʔδ

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  17. ػցֶशͷར༻Πϝʔδ
    Ϟσϧ

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  18. ػցֶशͷར༻Πϝʔδ
    ֶश
    ਪ࿦
    ਪ࿦Ϟσϧ

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  19. ֶशͱਪ࿦
    w ֶश
    w σʔλΛҰఆͷنଇ ΞϧΰϦζϜ
    ʹैͬͯϞσϧʹม׵
    w σʔλιʔε͕ॏཁͳͷͰɺ೔ʑೖΕସΘΔ΋ͷ͸ఆৗతʹֶश͢Δ
    w ਪ࿦
    w ϞσϧΛ࢖ͬͯɺ͋Δσʔλ͕Ͳͷύλʔϯʹ࠷΋͍͔ۙΛ൑அ͢Δ
    w ਪ࿦ʹ࢖͏Ϟσϧͷσʔλ઱౓͕ॏཁ

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  20. ֶशͱਪ࿦
    w ֶश
    w σʔλΛҰఆͷنଇ ΞϧΰϦζϜ
    ʹैͬͯϞσϧʹม׵
    w σʔλιʔε͕ॏཁͳͷͰɺ೔ʑೖΕସΘΔ΋ͷ͸ఆৗతʹֶश͢Δ
    w ਪ࿦
    w ϞσϧΛ࢖ͬͯɺ͋Δσʔλ͕Ͳͷύλʔϯʹ࠷΋͍͔ۙΛ൑அ͢Δ
    w ਪ࿦ʹ࢖͏Ϟσϧͷσʔλ઱౓͕ॏཁ
    8FCʹ͸ͳ͍ػցֶशج൫ͷϙΠϯτ


    Ϟσϧ͕ػցֶशʹ͓͚Δ಄೴ ϒϥοΫϘοΫε

    ϞσϧͷೖΕସ͑ͱɺֶशස౓ͳͲͷఆ͕ٛॏཁ

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  21. Πϯϑϥج൫ͷ࿩

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  22. ྫ͑͹͜ΜͳΞϓϦ͕͋ͬͨͱͯ͠
    8FC "QQ %#

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  23. ΞϓϦʹػցֶशΛ࢖ͬͨ

    ػೳΛ૿΍͢͜ͱΛߟ͑ͯΈΔ

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  24. Πϝʔδ͸͜͏
    8FC "QQ %#
    ਪ࿦Ϟσϧ
    ਪ࿦"1*ͷ

    αʔϏϯά

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  25. Πϝʔδ͸͜͏
    8FC "QQ %#
    ਪ࿦Ϟσϧ
    ਪ࿦"1*ͷ

    αʔϏϯά
    ͜ΕͰ΋ಈ͘ʹ͸ಈ͚͘Ͳɾɾɾ

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  26. Πϝʔδ͸͜͏
    8FC "QQ %#
    ਪ࿦Ϟσϧ
    ਪ࿦"1*ͷ

    αʔϏϯά
    Ϟσϧͷߋ৽Ͳ͏͢Δͷʁ

    ΞϓϦͷߋ৽ͱύΠϓϥΠϯ෼͚Δͷʁ
    Πϯϑϥઃܭ͸୭͕͢Δͷʁ

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  27. ػցֶशʹؔΘΔਓͨͪ ౰ࣾൺ

    w ϦαʔνϟʔˠΞϧΰϦζϜͱϞσϧධՁΛ୲౰
    w .-ΤϯδχΞ

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  28. ػցֶशʹؔΘΔਓͨͪ ౰ࣾൺ

    w Ϧαʔνϟʔ
    w .-ΤϯδχΞˠϞσϧΛ࢖ͬͨ"1*΍ΞϓϦͷϓϩτλΠϐϯάͳͲ

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  29. ػցֶशʹؔΘΔਓͨͪ ౰ࣾൺ

    w Ϧαʔνϟʔ
    w .-ΤϯδχΞˠϞσϧΛ࢖ͬͨ"1*΍ΞϓϦͷϓϩτλΠϐϯάͳͲ
    ࣗಈԽ͸Ͳͬͪͷ࢓ࣄʁ
    Πϯϑϥઃܭ͸ͩΕ͕΍Δʁ
    ύϑΥʔϚϯενϡʔχϯά͸ʁ

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  30. Two Big Problems
    Data
    Scientist
    Machine
    Learning
    Engineer
    vs

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  31. Ϟσϧ࡞੒ͱαʔϏϯάͷ

    ֞ࠜΛແͯ͘͠

    ࣮ӡ༻ʹ଱͑͏Δج൫Λ࡞Γ͍ͨ

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  32. .-0QTνʔϜͷϛογϣϯ ౰ࣾൺ

    w .-ΤϯδχΞ΍ݚڀऀ͕ػցֶशϞσϧͷ։ൃʹूதͰ͖Δ؀ڥΛఏڙ͢Δ
    w ϓϩτλΠϓΛϓϩμΫγϣϯϨϕϧʹҾ্͖͛Δ
    ˠݚڀऀɺ.-ΤϯδχΞ͕ͨͪ࡞ͬͨ΋ͷΛ࣮ࡍʹαʔϏεΠϯ͠ӡ༻·Ͱߦ͏

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  33. .-0QTͰิ͑Δ͜ͱ
    w ݚڀऀ͸ιϑτ΢ΣΞΤϯδχΞϦϯάͷ஌ݟ͕ͳ͍
    w +VQZUFS/PUFCPPL͚ͩ͋Ε͹͍͍ɻ%PDLFS΍Πϯϑϥ͸஌Βͳ͍͜ͱ΋ଟ͍
    w .-ΤϯδχΞ͸ϓϩτλΠϐϯά͕ಘҙͰ΋ܧଓతͳΞϓϦέʔγϣϯͷվળ΍
    ࣗಈԽͷλεΫ͸ઐ໳֎ͳ͜ͱ΋ଟ͍
    w $*$%ઃܭɺֶशϫʔΫϑϩʔͷ࠷దԽͳͲ͸༏ઌ౓͕௿͍

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  34. .-0QTͰิ͑Δ͜ͱ
    w %FW0QTͰഓͬͨϊ΢ϋ΢Λػցֶशʹ΋ద༻͍ͯ͘͠
    w (16$16Λ࢖ͬͨϫʔΫϑϩʔ΍ΠϯϑϥઃܭͳͲ͸ɺͦ΋ͦ΋Πϯϑϥͷ
    ஌͕ࣝཁٻ͞ΕΔ෼໺
    w ຊ൪ʹ޲͚ͨΞϓϦέʔγϣϯͷվળ
    w ίϯςφԽɺϘτϧωοΫͷվળͳͲ

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  35. ࣮ྫΛோΊͯΈΔ

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  36. ˜;0;05FDIOPMPHJFT *OD

    ΞʔΩςΫνϟશମ
    $MPVE-PBE
    #BMBODJOH
    $MPVE"SNPS
    ,VCFSOFUFT
    &OHJOF
    $MPVE
    4UPSBHF
    $POUBJOFS
    3FHJTUSZ
    $MPVE
    .FNPSZTUPSF
    %FWFMPQFS
    ը૾ετϨʔδ
    ϞσϧετϨʔδ
    ίϯςφΠϝʔδ
    Ωϟογϡ
    $MPVE
    $PNQPTFS
    ֶश
    ,VCFSOFUFT
    &OHJOF
    "//JOEFY
    Ϟσϧ
    6TFS
    ֶश
    ਪ࿦
    (16
    $MPVE
    'JMFTUPSF

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  37. .-0QTͷ୲౰ྖҬ ౰ࣾൺ

    w Πϯϑϥͷߏ੒؅ཧ $*$%ύΠϓϥΠϯؚΉ

    w ΞϓϦέʔγϣϯͷ$*$%΍ίϯςφԽɺνϡʔχϯά
    w ֶशϫʔΫϑϩʔͷվળ΍ӡ༻

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  38. શ෦Ͱ͖ͳ͍ͱ͍͚ͳ͍ͷ͔ʁ

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  39. νʔϜͷଟ༷ੑ
    w 43&ʹ͓͍ͯඞཁͱ͞ΕΔνʔϜͷଟ༷ੑ͸.-0QTͰ΋ॏཁ
    w ۀ຿΍ϓϩμΫτɺج൫ʹ͓͍ͯ࢖ΘΕΔٕज़͸શͯแׅతʹཧղ͢Δ
    ͜ͱ͕ॏཁͳͷ͸ݴ͏·Ͱ΋ͳ͍
    w ಘҙྖҬ͕ҧ͏ΤϯδχΞ͕ෳ਺͍Δ͜ͱʹΑͬͯνʔϜͱͯ͠

    ΑΓେ͖ͳൣғΛΧόʔͰ͖Δ
    w ҧ͏ࢹ఺ͷΤϯδχΞ͕͏·͘ಈ͍͍ͯ͘ʹ͸ɺݸʑͷνʔϜΛҙࣝͨ͠
    ಈ͖΍༏लͳϦʔμʔͷଘࡏ΋ॏཁ

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  40. νʔϜͷଟ༷ੑ
    w 43&ʹ͓͍ͯඞཁͱ͞ΕΔνʔϜͷଟ༷ੑ͸.-0QTͰ΋ॏཁ
    w ۀ຿΍ϓϩμΫτɺج൫ʹ͓͍ͯ࢖ΘΕΔٕज़͸શͯแׅతʹཧղ͢Δ
    ͜ͱ͕ॏཁͳͷ͸ݴ͏·Ͱ΋ͳ͍
    w ಘҙྖҬ͕ҧ͏ΤϯδχΞ͕ෳ਺͍Δ͜ͱʹΑͬͯνʔϜͱͯ͠

    ΑΓେ͖ͳൣғΛΧόʔͰ͖Δ
    w ҧ͏ࢹ఺ͷΤϯδχΞ͕͏·͘ಈ͍͍ͯ͘ʹ͸ɺݸʑͷνʔϜΛҙࣝͨ͠
    ಈ͖΍༏लͳϦʔμʔͷଘࡏ΋ॏཁ
    ɾνʔϜͷ౷཰
    ɾνʔϜͷଟ༷ੑ
    ͜ΕΒཁૉͷཱ͕྆ॏཁ

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  41. ΋͏গ࣮͠ࡍͷऔ૊Έʹ͍ͭͯ
    ۷ΓԼ͛ͯΈ·͠ΐ͏

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  42. .-0QTͷ୲౰ྖҬ ౰ࣾൺ

    w Πϯϑϥͷߏ੒؅ཧ $*$%ύΠϓϥΠϯؚΉ

    w ΞϓϦέʔγϣϯͷ$*$%΍ίϯςφԽɺνϡʔχϯά
    w ֶशϫʔΫϑϩʔͷվળ΍ӡ༻
    8FC։ൃͱڞ௨෦෼͕େ͖͍λεΫ
    ػցֶशʹݻ༗ͷ෦෼͕େ͖͍λεΫ

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  43. ΞϓϦͷνϡʔχϯά
    w ϏδωεϩδοΫΛม͑ͳ͍ൣғͰਪ࿦"1*ͷ଎౓Λ্͛Δ
    w "1.΍֎ܗ؂ࢹͳͲΛ͔ͭͬͯఆৗ؂ࢹ͠ɺϘτϧωοΫΛݟ͚ͭΔ
    w ϞσϧͰѻ͏ϕΫτϧͷύϥϝʔλʔ΍σʔλͷྔʹΑͬͯɺ$16
    (16ͲͪΒΛ࢖͏΂͖͔͕ܾ·Δ ෛՙࢼݧͳͲͰݕূ΋͢Δ

    w (16͸ศར͕ͩɺίϯςΩετεΠονʹऑ͍ͷͰΞϓϦέʔγϣϯͰ
    ͸ۃྗ࢖͍ͨ͘ͳ͍

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  44. ΞϓϦͷνϡʔχϯά
    w ϏδωεϩδοΫΛม͑ͳ͍ൣғͰਪ࿦"1*ͷ଎౓Λ্͛Δ
    w "1.΍֎ܗ؂ࢹͳͲΛ͔ͭͬͯఆৗ؂ࢹ͠ɺϘτϧωοΫΛݟ͚ͭΔ
    w ϞσϧͰѻ͏ϕΫτϧͷύϥϝʔλʔ΍σʔλͷྔʹΑͬͯɺ$16
    (16ͲͪΒΛ࢖͏΂͖͔͕ܾ·Δ ෛՙࢼݧͳͲͰݕূ΋͢Δ

    w (16͸ศར͕ͩɺίϯςΩετεΠονʹऑ͍ͷͰΞϓϦέʔγϣϯͰ
    ͸ۃྗ࢖͍ͨ͘ͳ͍
    *46$0/͸๻ͨͪʹେ੾ͳ͜ͱΛڭ͑ͯ͘Εͨ

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  45. ֶशϫʔΫϑϩʔͷӡ༻ͱվળ
    w ݚڀऀ΍.-ΤϯδχΞ͕ϞσϧΛ࡞੒͞ΕΔҰ࿈ͷྲྀΕΛ

    ϑϩʔʹམͱ͠ࠐΈɺࣗಈԽ͢Δ
    w ຖ೔ɺຖ࣌ؒ৘ใ͕มΘΔΑ͏ͳσʔλʹରͯ͠ܧଓͯ͠Ձ஋Λ

    ఏڙ͢ΔͨΊʹ͸ඞཁෆՄܽ
    w ฒྻ࣮ߦੑ͕Ͳ͜·ͰߴΊΒΕΔ͔
    w ΞϧΰϦζϜ΍ɺ࢖͍ͬͯΔख๏ʹର͢Δཧղ͕ॏཁ

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  46. ˜;0;05FDIOPMPHJFT *OD

    ը૾ݕࡧʹ࢖ΘΕΔҰൠతͳΞϧΰϦζϜ
    ෺ମݕग़ΞϧΰϦζϜ
    ● ը૾͔Β෺ମͷݕग़ͱΫϥε෼ྨΛ͢Δ
    ಛ௃ྔநग़ΞϧΰϦζϜ
    ● ը૾͔Βଟ࣍ݩϕΫτϧͷಛ௃ྔΛநग़͢Δ
    ۙࣅ࠶࠷ۙ๣୳ࡧ "//

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  47. .-ϓϩμΫγϣϯ·ͰͷྲྀΕ
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  48. .-ϓϩμΫγϣϯ·ͰͷྲྀΕ
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    ϞσϧσϓϩΠͯ͠

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  49. .-ϓϩμΫγϣϯ·ͰͷྲྀΕ
    ਪ࿦Ϟσϧ࡞ͬͨΑ
    ϞσϧσϓϩΠͯ͠

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    ϫʔΫϑϩʔͰ

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  50. վΊͯ.-0QTͷ

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  51. .-0QTνʔϜͷϛογϣϯ ౰ࣾൺ

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  52. .-0QTνʔϜͷϛογϣϯ ౰ࣾൺ

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    w ϓϩτλΠϓΛϓϩμΫγϣϯϨϕϧʹҾ্͖͛Δ
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  53. ·ͱΊ
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    w 43&͸%FW0QTΛؚΉɺαʔϏεͷఏڙՁ஋ΛߴΊΔ࣮ફతͳΞϓϩʔν
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  54. 5IBOLZPVGPSZPVSBUUFOUJPO

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