Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
機械学習基盤の本番運用とその取り組み / ML platform in production
Search
Sponsored
·
SiteGround - Reliable hosting with speed, security, and support you can count on.
→
Kohei Ota
December 08, 2019
Technology
2.1k
8
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
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
More Decks by Kohei Ota
See All by Kohei Ota
CloudNative Meets WebAssembly: Exploring Wasm's Potential to Replace Containers
inductor
4
3.6k
The Cloud Native Chronicles: 10 Years of Community Growth Inside and Outside Japan
inductor
0
200
Cracking the KubeCon CfP
inductor
2
930
KubeCon Recap -Platform migration at Scale-
inductor
1
1.1k
コンテナビルド最新事情 2022年度版 / Container Build 2022
inductor
3
610
データベースとストレージのレプリケーション入門 / Intro-of-database-and-storage-replication
inductor
28
6.7k
KubeConのケーススタディから振り返る、Platform for Platforms のあり方と その実践 / Lessons from KubeCon case studies: Platform for Platforms and its practice
inductor
3
1k
オンラインの技術カンファレンスを安定稼働させるための取り組み / SRE activity for online conference platform
inductor
1
1.4k
Kubernetesネットワーキング初級者脱出ガイド / Kubernetes networking beginner's guide
inductor
23
7.9k
Other Decks in Technology
See All in Technology
銀行勘定系システムにおける開発プロセス刷新×AIによる環境モダナイゼーション / Development Process Transformation and AI-Driven Environment Modernization
muit
1
2.6k
C#未経験の僕がAIに読めるコードを書かせるまで
maguroalternative
0
220
事業課題から技術的負債に向き合う
sansantech
PRO
2
2.2k
2026-09-08 そのJavaモダナイゼーション、AIに丸投げで大丈夫?IBM Bobで変わる品質と効率
yutanonaka
1
130
「どのログを見ればいい?」 から始めた サーバーレス障害解析
y_waka1
1
120
Why Agent Cost Needs Observability
nttcom
0
120
Vibe Coding で作ったプロダクトをどう安全に動かすか / How to Safely Run Products Built with Vibe Coding
glidenote
0
470
AIエージェントの権限管理 3: Agentic RAG の Fine grained access control 編
ren8k
0
190
GoのInterface内部構造から学ぶ!最高パフォーマンスを出すコード設計
yappli_developers
1
250
バイブコーディング時代のWebアプリ開発入門~Cloud Runで学ぶセキュアなビルドとデプロイ
waiwai2111
1
140
ADKで始める業務改善 - AIエージェント開発時の考えと設計
harappa80
2
260
白金鉱業Meetup Vol.25 アウトカムが二値のデータに対するCausal Impact
brainpadpr
0
280
Featured
See All Featured
Google's AI Overviews - The New Search
badams
0
1.6k
Highjacked: Video Game Concept Design
rkendrick25
PRO
1
460
The AI Search Optimization Roadmap by Aleyda Solis
aleyda
1
6.2k
Build The Right Thing And Hit Your Dates
maggiecrowley
39
3.4k
Producing Creativity
orderedlist
PRO
348
41k
4 Signs Your Business is Dying
shpigford
187
23k
Believing is Seeing
oripsolob
1
220
Gemini Prompt Engineering: Practical Techniques for Tangible AI Outcomes
mfonobong
2
540
Lightning Talk: Beautiful Slides for Beginners
inesmontani
PRO
2
700
Exploring anti-patterns in Rails
aemeredith
4
510
The SEO identity crisis: Don't let AI make you average
varn
0
560
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
danielanewman
230
23k
Transcript
ػցֶशج൫ͷຊ൪ӡ༻ͱ ͦͷऔΓΈ 1SFTFOUFECZ,PIFJ0UB !JOEVDUPS +VMZ5FDI'FTUB
ࣗݾհ w ଠాߤฏ !@JOEVDUPS@ !JOEVDUPS w %PDLFS.FFUVQ5PLZP0SHBOJ[FS w *OGSBTUSVDUVSF&OHJOFFS
.-0QT5FBN!;0;05FDIOPMPHJFT *OD
;0;0508/ͷը૾ݕࡧػೳʹΈΔ ,VCFSOFUFTΛͬͨػցֶशج൫ӡ༻ͷཪଆ $MPVE/BUJWF%BZT,BOTBJ $PQZSJHIU;0;05FDIOPMPHJFT *OD גࣜձࣾ;0;0ςΫϊϩδʔζ ։ൃ෦ .-0QTνʔϜ ଠా
ߤฏ
;0;0508/ͷը૾ݕࡧػೳʹΈΔ ,VCFSOFUFTΛͬͨػցֶशج൫ӡ༻ͷཪଆ $MPVE/BUJWF%BZT,BOTBJ $PQZSJHIU;0;05FDIOPMPHJFT *OD גࣜձࣾ;0;0ςΫϊϩδʔζ ։ൃ෦ .-0QTνʔϜ ଠా
ߤฏ ৄ͘͠4QFBLFSEFDLͷεϥΠυͰʂ
ࠓͷλʔήοτ w Πϯϑϥ%FW0QT43&తͳ͜ͱΛ͍ͬͯΔਓ w ػցֶशʹ͍ͭͯ͋·Γྑ͔͍ͬͯ͘ͳ͍ਓ શʹཧղ͍ͯ͠Δਓ w ಥવৼΒΕΔṖͷλεΫʹରԠͰ͖Δ͔Θ͔Βͳ͍ਓ
͋Δ͜Μͳ͜ͱΛݴΘΕΔͱ ߟ͑ͯΈ͍ͯͩ͘͞
ܦӦऀʮ͜Ε͔Β"*ͩʂʂʯ
"*ʁػցֶशʁͲ͏ҧ͏ͷʁ
ػցֶशͬͯͳʹʁʁ
ػցֶशʁʁ w σʔλΛೖྗɺ͋Δ͖݁ՌΛग़ྗͱͯ͠ w ෳͷσʔλ͔Βڞ௨ͷϧʔϧಛɺஅج४Λநग़ w ໌ࣔతʹϓϩάϥϜϩδοΫΛॻ͔ͣʹ݁ՌΛग़ͨ͢ΊͷΈ
ػցֶशʁʁ w σʔλΛೖྗɺ͋Δ͖݁ՌΛग़ྗͱͯ͠ w ෳͷσʔλ͔Βڞ௨ͷϧʔϧಛɺஅج४Λநग़ w ໌ࣔతʹϓϩάϥϜϩδοΫΛॻ͔ͣʹ݁ՌΛग़ͨ͢ΊͷΈ ϩδοΫͷநԽ
ͳͥػցֶश͕ٻΊΒΕΔͷ͔ w ϞόΠϧΠϯλʔωοτ͕ීٴ͖ͬͨ͠ࠓɺࣄͷঢ়گٸܹʹมΘΔ w ͦͷมԽΛϓϩάϥϜͰද͢ͷେม w *GGPSͰઃఆͨ͠ύϥϝʔλʔΛॻ͖͑Δ͚ͩͷվमΛՃ͑Δͷ ͍͠͠ɺޮՌଌఆେมʢ͍ΘΏΔzਓແೳzͱ͍͏ݺΕΔͭʣ w ͜Ε·Ͱͷ͔Β࣍ͷ݁ՌΛ༧ଌ͢Δࣄ͕Ͱ͖Δശ͕͋Ε
ຊ࣭తʹαʔϏεͷϩδοΫͰͳ͍ͣ
ͳͥػցֶश͕ٻΊΒΕΔͷ͔ w ϞόΠϧΠϯλʔωοτ͕ීٴ͖ͬͨ͠ࠓɺࣄͷঢ়گٸܹʹมΘΔ w ͦͷมԽΛϓϩάϥϜͰද͢ͷେม w *GGPSͰઃఆͨ͠ύϥϝʔλʔΛॻ͖͑Δ͚ͩͷվमΛՃ͑Δͷ ͍͠͠ɺޮՌଌఆେมʢ͍ΘΏΔzਓແೳzͱ͍͏ݺΕΔͭʣ w ͜Ε·Ͱͷ͔Β࣍ͷ݁ՌΛ༧ଌ͢Δࣄ͕Ͱ͖Δശ͕͋Ε
ຊ࣭తʹαʔϏεͷϩδοΫͰͳ͍ͣ ػցֶशʹΑͬͯಘΒΕͨϞσϧ ϒϥοΫϘοΫε Λ ೖΕସ͑Δ͚ͩͰΞϓϦέʔγϣϯ͕ੈͷதͷ χʔζʹରԠͰ͖ΔͷͰͳ͍͔ʂͱ͍͏ൃ
ػցֶशͷԠ༻ྫ w ݕࡧΤϯδϯͷϦίϝϯσʔγϣϯ w ؞ʹ͓͚ΔෆྑͳͲͷҟৗݕ w Իೝࣝɺը૾ೝࣝ w גՁ༧ଌ
ػցֶशͷԠ༻ྫ w ݕࡧΤϯδϯͷϦίϝϯσʔγϣϯ w ؞ʹ͓͚ΔෆྑͳͲͷҟৗݕ w Իೝࣝɺը૾ೝࣝ w גՁ༧ଌ ϦΞϧλΠϜͰมԽ͢Δͷ
ਓؒͷखΛհ͢Δͱେมͳͷʹରͯ͠ಛʹ༗ޮ
ػցֶशͷར༻Πϝʔδ
ػցֶशͷར༻Πϝʔδ Ϟσϧ
ػցֶशͷར༻Πϝʔδ ֶश ਪ ਪϞσϧ
ֶशͱਪ w ֶश w σʔλΛҰఆͷنଇ ΞϧΰϦζϜ ʹैͬͯϞσϧʹม w σʔλιʔε͕ॏཁͳͷͰɺʑೖΕସΘΔͷఆৗతʹֶश͢Δ w
ਪ w ϞσϧΛͬͯɺ͋Δσʔλ͕Ͳͷύλʔϯʹ࠷͍͔ۙΛஅ͢Δ w ਪʹ͏Ϟσϧͷσʔλ͕ॏཁ
ֶशͱਪ w ֶश w σʔλΛҰఆͷنଇ ΞϧΰϦζϜ ʹैͬͯϞσϧʹม w σʔλιʔε͕ॏཁͳͷͰɺʑೖΕସΘΔͷఆৗతʹֶश͢Δ w
ਪ w ϞσϧΛͬͯɺ͋Δσʔλ͕Ͳͷύλʔϯʹ࠷͍͔ۙΛஅ͢Δ w ਪʹ͏Ϟσϧͷσʔλ͕ॏཁ 8FCʹͳ͍ػցֶशج൫ͷϙΠϯτ Ϟσϧ͕ػցֶशʹ͓͚Δ಄ ϒϥοΫϘοΫε ϞσϧͷೖΕସ͑ͱɺֶशසͳͲͷఆ͕ٛॏཁ
Πϯϑϥج൫ͷ
ྫ͑͜ΜͳΞϓϦ͕͋ͬͨͱͯ͠ 8FC "QQ %#
ΞϓϦʹػցֶशΛͬͨ ػೳΛ૿͢͜ͱΛߟ͑ͯΈΔ
Πϝʔδ͜͏ 8FC "QQ %# ਪϞσϧ ਪ"1*ͷ αʔϏϯά
Πϝʔδ͜͏ 8FC "QQ %# ਪϞσϧ ਪ"1*ͷ αʔϏϯά ͜ΕͰಈ͘ʹಈ͚͘Ͳɾɾɾ
Πϝʔδ͜͏ 8FC "QQ %# ਪϞσϧ ਪ"1*ͷ αʔϏϯά Ϟσϧͷߋ৽Ͳ͏͢Δͷʁ ΞϓϦͷߋ৽ͱύΠϓϥΠϯ͚Δͷʁ Πϯϑϥઃܭ୭͕͢Δͷʁ
ػցֶशʹؔΘΔਓͨͪ ࣾൺ w ϦαʔνϟʔˠΞϧΰϦζϜͱϞσϧධՁΛ୲ w .-ΤϯδχΞ
ػցֶशʹؔΘΔਓͨͪ ࣾൺ w Ϧαʔνϟʔ w .-ΤϯδχΞˠϞσϧΛͬͨ"1*ΞϓϦͷϓϩτλΠϐϯάͳͲ
ػցֶशʹؔΘΔਓͨͪ ࣾൺ w Ϧαʔνϟʔ w .-ΤϯδχΞˠϞσϧΛͬͨ"1*ΞϓϦͷϓϩτλΠϐϯάͳͲ ࣗಈԽͲͬͪͷࣄʁ ΠϯϑϥઃܭͩΕ͕Δʁ ύϑΥʔϚϯενϡʔχϯάʁ
Two Big Problems Data Scientist Machine Learning Engineer vs
Ϟσϧ࡞ͱαʔϏϯάͷ ֞ࠜΛແͯ͘͠ ࣮ӡ༻ʹ͑͏Δج൫Λ࡞Γ͍ͨ
%FWY0QT
.-Y0QT
.-0QTνʔϜͷϛογϣϯ ࣾൺ w .-ΤϯδχΞݚڀऀ͕ػցֶशϞσϧͷ։ൃʹूதͰ͖ΔڥΛఏڙ͢Δ w ϓϩτλΠϓΛϓϩμΫγϣϯϨϕϧʹҾ্͖͛Δ ˠݚڀऀɺ.-ΤϯδχΞ͕ͨͪ࡞ͬͨͷΛ࣮ࡍʹαʔϏεΠϯ͠ӡ༻·Ͱߦ͏
.-0QTͰิ͑Δ͜ͱ w ݚڀऀιϑτΣΞΤϯδχΞϦϯάͷݟ͕ͳ͍ w +VQZUFS/PUFCPPL͚ͩ͋Ε͍͍ɻ%PDLFSΠϯϑϥΒͳ͍͜ͱଟ͍ w .-ΤϯδχΞϓϩτλΠϐϯά͕ಘҙͰܧଓతͳΞϓϦέʔγϣϯͷվળ ࣗಈԽͷλεΫઐ֎ͳ͜ͱଟ͍ w $*$%ઃܭɺֶशϫʔΫϑϩʔͷ࠷దԽͳͲ༏ઌ͕͍
.-0QTͰิ͑Δ͜ͱ w %FW0QTͰഓͬͨϊϋΛػցֶशʹద༻͍ͯ͘͠ w (16$16ΛͬͨϫʔΫϑϩʔΠϯϑϥઃܭͳͲɺͦͦΠϯϑϥͷ ͕ࣝཁٻ͞ΕΔ w ຊ൪ʹ͚ͨΞϓϦέʔγϣϯͷվળ w ίϯςφԽɺϘτϧωοΫͷվળͳͲ
࣮ྫΛோΊͯΈΔ
;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
None
.-0QTͷ୲ྖҬ ࣾൺ w Πϯϑϥͷߏཧ $*$%ύΠϓϥΠϯؚΉ w ΞϓϦέʔγϣϯͷ$*$%ίϯςφԽɺνϡʔχϯά w ֶशϫʔΫϑϩʔͷվળӡ༻
શ෦Ͱ͖ͳ͍ͱ͍͚ͳ͍ͷ͔ʁ
:FTBOEOP
νʔϜͷଟ༷ੑ w 43&ʹ͓͍ͯඞཁͱ͞ΕΔνʔϜͷଟ༷ੑ.-0QTͰॏཁ w ۀϓϩμΫτɺج൫ʹ͓͍ͯΘΕΔٕज़શͯแׅతʹཧղ͢Δ ͜ͱ͕ॏཁͳͷݴ͏·Ͱͳ͍ w ಘҙྖҬ͕ҧ͏ΤϯδχΞ͕ෳ͍Δ͜ͱʹΑͬͯνʔϜͱͯ͠ ΑΓେ͖ͳൣғΛΧόʔͰ͖Δ w
ҧ͏ࢹͷΤϯδχΞ͕͏·͘ಈ͍͍ͯ͘ʹɺݸʑͷνʔϜΛҙࣝͨ͠ ಈ͖༏लͳϦʔμʔͷଘࡏॏཁ
νʔϜͷଟ༷ੑ w 43&ʹ͓͍ͯඞཁͱ͞ΕΔνʔϜͷଟ༷ੑ.-0QTͰॏཁ w ۀϓϩμΫτɺج൫ʹ͓͍ͯΘΕΔٕज़શͯแׅతʹཧղ͢Δ ͜ͱ͕ॏཁͳͷݴ͏·Ͱͳ͍ w ಘҙྖҬ͕ҧ͏ΤϯδχΞ͕ෳ͍Δ͜ͱʹΑͬͯνʔϜͱͯ͠ ΑΓେ͖ͳൣғΛΧόʔͰ͖Δ w
ҧ͏ࢹͷΤϯδχΞ͕͏·͘ಈ͍͍ͯ͘ʹɺݸʑͷνʔϜΛҙࣝͨ͠ ಈ͖༏लͳϦʔμʔͷଘࡏॏཁ ɾνʔϜͷ౷ ɾνʔϜͷଟ༷ੑ ͜ΕΒཁૉͷཱ͕྆ॏཁ
͏গ࣮͠ࡍͷऔΈʹ͍ͭͯ ۷ΓԼ͛ͯΈ·͠ΐ͏
.-0QTͷ୲ྖҬ ࣾൺ w Πϯϑϥͷߏཧ $*$%ύΠϓϥΠϯؚΉ w ΞϓϦέʔγϣϯͷ$*$%ίϯςφԽɺνϡʔχϯά w ֶशϫʔΫϑϩʔͷվળӡ༻
8FC։ൃͱڞ௨෦͕େ͖͍λεΫ ػցֶशʹݻ༗ͷ෦͕େ͖͍λεΫ
ΞϓϦͷνϡʔχϯά w ϏδωεϩδοΫΛม͑ͳ͍ൣғͰਪ"1*ͷΛ্͛Δ w "1.֎ܗࢹͳͲΛ͔ͭͬͯఆৗࢹ͠ɺϘτϧωοΫΛݟ͚ͭΔ w ϞσϧͰѻ͏ϕΫτϧͷύϥϝʔλʔσʔλͷྔʹΑͬͯɺ$16 (16ͲͪΒΛ͏͖͔͕ܾ·Δ ෛՙࢼݧͳͲͰݕূ͢Δ
w (16ศར͕ͩɺίϯςΩετεΠονʹऑ͍ͷͰΞϓϦέʔγϣϯͰ ۃྗ͍ͨ͘ͳ͍
ΞϓϦͷνϡʔχϯά w ϏδωεϩδοΫΛม͑ͳ͍ൣғͰਪ"1*ͷΛ্͛Δ w "1.֎ܗࢹͳͲΛ͔ͭͬͯఆৗࢹ͠ɺϘτϧωοΫΛݟ͚ͭΔ w ϞσϧͰѻ͏ϕΫτϧͷύϥϝʔλʔσʔλͷྔʹΑͬͯɺ$16 (16ͲͪΒΛ͏͖͔͕ܾ·Δ ෛՙࢼݧͳͲͰݕূ͢Δ
w (16ศར͕ͩɺίϯςΩετεΠονʹऑ͍ͷͰΞϓϦέʔγϣϯͰ ۃྗ͍ͨ͘ͳ͍ *46$0/ͨͪʹେͳ͜ͱΛڭ͑ͯ͘Εͨ
ֶशϫʔΫϑϩʔͷӡ༻ͱվળ w ݚڀऀ.-ΤϯδχΞ͕ϞσϧΛ࡞͞ΕΔҰ࿈ͷྲྀΕΛ ϑϩʔʹམͱ͠ࠐΈɺࣗಈԽ͢Δ w ຖɺຖ࣌ؒใ͕มΘΔΑ͏ͳσʔλʹରͯ͠ܧଓͯ͠ՁΛ ఏڙ͢ΔͨΊʹඞཁෆՄܽ w ฒྻ࣮ߦੑ͕Ͳ͜·ͰߴΊΒΕΔ͔ w
ΞϧΰϦζϜɺ͍ͬͯΔख๏ʹର͢Δཧղ͕ॏཁ
;0;05FDIOPMPHJFT *OD ը૾ݕࡧʹΘΕΔҰൠతͳΞϧΰϦζϜ ମݕग़ΞϧΰϦζϜ • ը૾͔Βମͷݕग़ͱΫϥεྨΛ͢Δ ಛྔநग़ΞϧΰϦζϜ • ը૾͔Βଟ࣍ݩϕΫτϧͷಛྔΛநग़͢Δ
ۙࣅ࠶࠷ۙ୳ࡧ "// • ߴʹଟ࣍ݩͷϕΫτϧΛ୳ࡧ͢Δ IUUQTHJUIVCDPNTQPUJGZBOOPZ $// 'FBUVSF
.-ϓϩμΫγϣϯ·ͰͷྲྀΕ ਪϞσϧ࡞ͬͨΑ ϞσϧσϓϩΠͯ͠ ਪ"1*࡞ΔΑʂ
.-ϓϩμΫγϣϯ·ͰͷྲྀΕ ਪϞσϧ࡞ͬͨΑ ϞσϧσϓϩΠͯ͠ ਪ"1*࡞ΔΑʂ ϫʔΫϑϩʔͰ ਪϞσϧͷੜΛࣗಈԽ ΞϓϦέʔγϣϯͷ $*$%ύΠϓϥΠϯ ਪϞσϧͷ ࣗಈσϓϩΠ
.-ϓϩμΫγϣϯ·ͰͷྲྀΕ ਪϞσϧ࡞ͬͨΑ ϞσϧσϓϩΠͯ͠ ਪ"1*࡞ΔΑʂ ϫʔΫϑϩʔͰ ਪϞσϧͷੜΛࣗಈԽ ΞϓϦέʔγϣϯͷ $*$%ύΠϓϥΠϯ ਪϞσϧͷ ࣗಈσϓϩΠ
࣮ɺ͍ͬͯΔ͜ͱͷຊ࣭ ࠓ·Ͱͷ43&%FW0QTͷख๏ͱͦΜͳʹҧ͍͕ͳ͍
վΊͯ.-0QTͷ ϛογϣϯʹ͍ͭͯৼΓฦΔ
.-0QTνʔϜͷϛογϣϯ ࣾൺ w .-ΤϯδχΞݚڀऀ͕ػցֶशϞσϧͷ։ൃʹूதͰ͖ΔڥΛఏڙ͢Δ w ϓϩτλΠϓΛϓϩμΫγϣϯϨϕϧʹҾ্͖͛Δ ˠݚڀऀɺ.-ΤϯδχΞ͕ͨͪ࡞ͬͨͷΛ࣮ࡍʹαʔϏεΠϯ͠ӡ༻·Ͱߦ͏
.-0QTνʔϜͷϛογϣϯ ࣾൺ w .-ΤϯδχΞݚڀऀ͕ػցֶशϞσϧͷ։ൃʹूதͰ͖ΔڥΛఏڙ͢Δ w ϓϩτλΠϓΛϓϩμΫγϣϯϨϕϧʹҾ্͖͛Δ ˠݚڀऀɺ.-ΤϯδχΞ͕ͨͪ࡞ͬͨͷΛ࣮ࡍʹαʔϏεΠϯ͠ӡ༻·Ͱߦ͏ Ϟσϧ"1*ͷʑͷ։ൃऀ ମݧͷ্ 43&తͳΞϓϩʔν
·ͱΊ w %FW0QT։ൃͱӡ༻ͷ֞ࠜΛແ͘͠ɺϏδωεʹߩݙ͢ΔεϐʔυײΛߴΊΔͨΊ ͷऔΓΈɺจԽվֵͷϚΠϯυηοτͩͬͨ w 43&%FW0QTΛؚΉɺαʔϏεͷఏڙՁΛߴΊΔ࣮ફతͳΞϓϩʔν w .-0QT྆ऀͷ࣋ͭಛੑΛػցֶशͷεϐʔυײ৴པੑΛߴΊΔͨΊͷऔΓΈ
5IBOLZPVGPSZPVSBUUFOUJPO