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
NDS meetup in Niigata #1
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
kasacchiful
PRO
July 13, 2014
240
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
NDS meetup in Niigata #1
2014/07/13 第1回NDS新潟出張版で発表した資料
kasacchiful
PRO
July 13, 2014
More Decks by kasacchiful
See All by kasacchiful
俺流 AWS Step Functions 構築の流儀 / 20261005shinagawakai-aws-step-functions
kasacchiful
PRO
1
86
AWS Step Functions 大規模並列の壁を越える / jaws-sonic-2026-niigata-step-functions
kasacchiful
PRO
1
600
Step Functions Express ワークフローの使い所 / 20260725jawsug-niigata-sado
kasacchiful
PRO
1
140
Step FunctionsでAIエージェント × 人の承認を試す / 20260705jawsug-hokurikushinkansen-agentcore-hitl-workflow
kasacchiful
PRO
0
130
上越のサメ食文化を訪ねて - 新潟市民の初体験レポ / ssmjp-shark
kasacchiful
PRO
1
89
Rust on AWS でデータ分析 / 20260523iotlt-niigata-rust-on-aws
kasacchiful
PRO
0
71
Step Functionsで始めるサーバーレス入門 〜 つないで動かすAWSサーバーレス
kasacchiful
PRO
0
80
Amazon Q Developer CLI (現Kiro CLI) で作った 新潟ランチマップWebアプリのこれまでとこれから / 20260207jawsug-tochigi
kasacchiful
PRO
0
150
Amazon SageMaker Catalogの、AIエージェントによる自動データ分類機能を試してみようとしたが、できなかったので、代わりに最近構築したデータ連携基盤を紹介します / 20260117jawsug-fukui
kasacchiful
PRO
0
210
Featured
See All Featured
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
35
2.6k
Learning to Love Humans: Emotional Interface Design
aarron
275
41k
Faster Mobile Websites
deanohume
310
32k
StorybookのUI Testing Handbookを読んだ
zakiyama
31
7k
My Coaching Mixtape
mlcsv
0
340
Rebuilding a faster, lazier Slack
samanthasiow
85
9.7k
The untapped power of vector embeddings
frankvandijk
2
1.9k
Scaling GitHub
holman
464
140k
YesSQL, Process and Tooling at Scale
rocio
174
15k
Why Mistakes Are the Best Teachers: Turning Failure into a Pathway for Growth
auna
0
320
How Fast Is Fast Enough? [PerfNow 2025]
tammyeverts
3
920
Visual Storytelling: How to be a Superhuman Communicator
reverentgeek
2
700
Transcript
Χϥμվܭը ংষ !LBTBDDIJGVM /%4NFFUVQJO/JJHBUB
ҙ
͜ͷൃදͰɺ ٕज़తͳ͜ͱ ग़͖ͯ·ͤΜɻ
None
ࣗݾհ ּݪɹʢ!LBTBDDIJGVMʣ 8FCɺۀΞϓϦɺΠϯϑϥ 3VCZ͕͓ؾʹೖΓ ࠷ۙKT͍͡Γ͕ଟ͍ /%4ྺ ڈͷʢ"NB[PO8FC 4FSWJDFϋϯζΦϯʣ͔Βࠒ ߹͍ݟͯࢀՃத ʢ͡ΊͯͷʙʣͰ3VCZ
ωλͰൃද
ຊηογϣϯͷ ϙΠϯτ
ݱࡏɺΧϥμվܭը ࣮ࢪத
ͲͷΑ͏ʹɺ ܭըɾ࣮ߦ͍ͯ͠Δ͔ આ໌͠·͢
ͳΜ͔ͩΜͩͰ γεςϜ։ൃܭըͱ Χϥμվܭը جຊతʹҰॹʂ
Ωʔϫʔυ
,(*,1* ΠςϨʔγϣϯ ;Γ͔͑Γ
ݱঢ়ͱՌ
ݱঢ় ݄͔ΒؒͷΧϥμվܭը ݱࡏɺ։͔࢝Βϲ݄͕ܦա ·ͩ࢝·͔ͬͨΓ
ൺֱ ։࢝ɿ݄ʢʣ ମॏɿLH ମࢷɿ #.*ɿ ϲ݄ޙɿ݄ʢʣ ମॏɿLH ମࢷɿ #.*ɿ ϲ݄ͰLHͷݮྔʹޭ
ಈػ
ମྗ͕ແ͍ ϋΝϋΝͱɺ͙͢ଉݣ͍͕ߥ͘ͳΔ ےྗ͕ແ͍ ίʔώʔͱ৯͕ஔ͔ΕͨτϨΠΛยखͰ࣋ͭͱɺϓϧϓϧͯ͠͠·͏ɻ ใॲཧͷࢼݧͱ͔डݧ͢Δͱɺख͕ͭΔɻখจॻ͚ͳ͍ɻ㱺߹֨Ͱ͖ͳ͍ɻ ଠΓ͗͢ ݄ʹLH౸ୡɻॏ͍ɻ ੲόεέͷΰʔϧϦϯάʹಧ͍͚ͨͲɺࠓΰʔϧωοτʹ͢Βશવಧ͔ͳ͍ ಈػ
ͳΜͱ͔ͤͶ
͔ͤͬ͘ͳͷͰɺγεςϜ ։ൃܭը෩ʹͬͯΈΑ͏ʂ
ԿΛ͢Δ͔ w తͱඪઃఆ w ܭը w ࣮ߦ w ;Γ͔͑Γ 1%$"Λ·Θͯ͠ΈΔ
վͷత
త ମΛ݈߁ମʹ͢Δ ए͍͜Ζʢֶੜ࣌ʣͷମܕʹ͢
ඪ ,(*ͱ,1*Λઃఆ /%4 (PPHMF"OBMZUJDT ͷճͰొ త͔Β۩ମతͳͱͳΔඪམͱ͠ࠐΉ
,(* ,FZ(PBM*OEJDBUPSॏཁඪୡࢦඪ తΛୡ͢ΔͨΊͷɺ۩ମతͳඪΛઃఆ͢Δɻ ྫɿ ޙͷܾࢉ࣌ʹɺച্ߴஹԁΛୡ͢Δ ϖʔδϏϡʔΛޙʹສ17ʹ͢Δ ϲ݄Ͱஷۚສԁʹ͢Δ ʮԿʯΛʮ͍ͭʯୡ͢Δ͔
,1* ,FZ1FSGPSNBODF*OEJDBUPSॏཁۀධՁࢦඪ ඪʹ݁͢Δࢦඪͷ͜ͱ ྫ͑ɺ,(*͕ʮޙͷܾࢉ࣌ʹɺച্ߴஹԁΛୡ͢Δʯ ͱͨ͠߹ɺ,1*ͱͯ͠ʮ݄࣍ɾ࢛ظ͝ͱͷച্ߴɺҾ͖߹ ͍Ҋ݅ɺސ٬๚ճɺาཹ·Γɺղ݅ʯΛઃఆ͢ Δ ,(*͕࠷ऴඪͳΒɺ,1*్தܦաͷࢦඪ㱺ਐḿ֬ೝ ཻͷେ͖͍λεΫΛɺࡉԽ͢Δ࡞ۀʹࣅ͍ͯΔ
,1* ,1*ಛఆʹɺʮ4."35ʯͱ͍͏ཁૉ͕ඞཁ 4QFDJpDʢ໌ྎੑʣ .FBTVSBCMFʢܭྔੑʣ "DIJFWBCMFʢୡՄೳੑʣ 3FTVMUPSJFOUFEPS3FMFWBOUʢ݁Ռࢦ·ͨؔ࿈ੑʣ 5JNFCPVOEʢظݶʣ ग़యɿ8JLJQFEJBʮॏཁۀධՁࢦඪʯ IUUQKBXJLJQFEJBPSHXJLJ&%&"&""%&##&&"&#&"&$&"
ඪ ,(* ମॏΛͰLHະຬʹ ମࢷΛͰະຬʹ LNͷ ,1* ମॏLH͔Βϲ݄ʹLHݮ ମࢷΛ͔Βϲ݄ʹݮ ϥϯχϯάΛLNIϖʔεͰ ΕΔΑ͏ʹ͢Δ
,(*,1*ͷઃఆҰ͖ΓͰͳ͘ɺ ఆظతͳݟ͠Λͯ֬͠ΛߴΊ͍ͯ͘
͏গ͠ࡉԽ ͙͢ʹLNIϖʔεͰΕΔΑ͏ʹͳΔΘ͚͡Όͳ͍ ͷͰɺ࣮ࡍͷτϨʔχϯάͰɺͬͱࡉԽͯ͠Έ Δ LNIΛͰԿຊΕΔ͔ʢճ61ଌఆʣ LNIΛԿܧଓతʹΕΔ͔ʢ࣌ؒ61ଌఆʣ LNIΛ্͛ͯԿʢʣܧଓత ʹΕΔ͔ʢ61ଌఆʣ
ےྗʹؔ͢Δ,1*ʁ ےྗʹؔ͢Δ,1*ɺͱΓ͋͑ͣݟૹͬͨ ݱࡏͲͷ͙Β͍ग़དྷͯɺޙʹͲͷ͘Β͍Ͱ ͖Δͷ͔ෆ໌ͩͬͨͨΊ ޙड़ͷΠςϨʔγϣϯຖͷઃఆͰɺ͍ͣΕઃఆ͢Δ
,1*͔Β۩ମతͳߦಈ ඪ͔Β,1*Λ۷ΓԼ͛ͯߟ͑ Δ߹ͱɺߦಈ͔Β,1*Λ۷Γ Լ͛ͯߟ͑Δ߹͕͋Γ·͢ɻ ࣗͰΓ͍͢ํ๏Λߟ͑ͯ Έ·͠ΐ͏ɻ ,1*Λߟ͑ΔͱɺΔ͖ߦಈ ͕ݟ͑ͯ͘Δ 100kg6B%25kg 1:C
*GYV\E A: 0 /E: C 10km,C 1500m8 50km(.-E9 8 FXS\LED ;?2=7F@ =7C )EA : OX\P[J E:C &<100 M[SW20kg10 '100 $100 KIZNO20 "20 GYV\E B: >%#E B: U\T[5C Q[R\H\ 5C !5C >+E B: B: 43EB :
ܭը
ܭը ,(*,1*ΛݩʹܭըΛ࿅Δ ظؒຖͷΠςϨʔγϣϯͰߟ͑Δ ࠓճϲ݄ຖͷΠςϨʔγϣϯͱͨ͠ ຊདྷɺिؒͱ͔͕ཧ͕ͩɺिؒ͡Όମͷ มԽ͕΄ͱΜͲͳ͍ʢޡࠩͷൣғʣͱஅ
֤ΠςϨʔγϣϯͷ࡞ۀ • KGI • KPI • •
• • • "" • KPT • !$%&'(#
࣮ߦܭըઃఆ ӡಈ ӡಈ༧ఆΛ͋Β͔͡Ί(PPHMFΧϨϯμʔʹొ ӡಈ߲Λ࣮ࢪલʹ͋ΔఔܾΊ͓ͯ͘ ৯ࣄ ৯ࣄ༰ͷݟ͠ ه هͷ༰ɺํ๏ͷݕ౼
100kg6B%25kg 1:C *GYV\E A: 0 /E: C 10km,C 1500m8 50km(.-E9
8 FXS\LED ;?2=7F@ =7C )EA : OX\P[J E:C &<100 M[SW20kg10 '100 $100 KIZNO20 "20 GYV\E B: >%#E B: U\T[5C Q[R\H\ 5C !5C >+E B: B: 43EB :
ܭըௐɺଥڠҊ ମௐෆྑɺ༧ఆ֎ͷࣄʹରԠͰ͖ΔΑ͏ʹɺܭըʹ༨༟Λ࣋ͨͤΔ ࣄ͕සൃͯ͠ରԠ͖͠Εͳ͍߹ܭըࣗମΛݟ͢ʢϦεέʣ ۀ͕ਂ·ͰٴͿɹɹӡಈ࣮ࢪͷௐ ҿΈձ͕ଟ͍ɹɹɹɹɹࢀՃ͠ͳ͍ɺӡಈڧௐ ܭը࣮ߦʹ੍ؔͯ͠ݶࣄ߲Λઃ͚Δ͕ɺ͋ΔఔͷଥڠҊߟ͑Δ ετϨεΛͨΊͳ͍ ϥʔϝϯ߇͑ΔɹɹͲ͏ͯ͠৯͍ͨ߹݄ҰճͰεʔϓҿΈͳ͠ ϚοΫ߇͑ΔɹɹɹͲ͏ͯ͠৯͍ͨ߹ϑΟϨΦϑΟογϡͷΈ
࣮ߦ
ӡಈ ےτϨ δϜτϨʔχϯά ࣗͰɺεΫϫοτத ৺ NJO8PSLPVU ༗ࢎૉӡಈ ࠷ॳΥʔΩϯάͷΈ ຖͷΠϯλʔόϧ LN
Ҏ্ඞ࣮ͣࢪ ʮࣗͷݶքͷͪΐͬͱઌʯΛݟఆΊΔͱޮՌతʢͩͱײ͍ͯ͡Δʣ
৯ࣄ ேϓϩςΠϯ Ϗλϛϯิڅ Ͱ؆୯ʹࡁ·ͯ͠Δ னΧϩϦʔͰ͓ෲ͕;͘ ΕΔͷΛத৺ʹ ͕ͬͭΓ৯ͳ͍ ͍ίʔώʔɺϥʔϝϯɺϋϯ όʔΨʔېࢭ ே
ϓϩςΠϯͱαϓϦϝϯτ ϓϩςΠϯͱࡊδϡʔε ன 4VCXBZͷαϯυΠον 5VMMZ`T$P⒎FFͷϥϯν ίϯϏχͷೲ౾ר͖ͱαϥμ λϯύΫ࣭ͱࡊத৺ϝχϡʔ ۀ࣌ίϯϏχͷೲ౾ר͖ αϥμͰ
ه ࣮ͦΜͳʹ·Ίʹॻ͍ͯͳ͍ ຖॻ͘ؾྗ͕ແ͍ ·ͱΊॻ͖ͳͷͰɺൈ͚࿙Ε͋Γ γεςϜ։ൃͰɺͦͷΑ͏ʹͳΒͳ͍Α͏ʹɺͦͷॻ͖͘ .JDSPTPGU0OF%SJWF্ͷ&YDFMʹͱΓ͋͑ͣϝϞ ຊདྷɺτϨʔχϯά࣮ફதʹهड़͖͢ ݸਓతʹूதͯ͠τϨʔχϯά͍ͨ͠ͷͰɺτϨʔχϯάதJ1IPOF͍͡Βͳ ͍Α͏ʹ͍ͯ͠Δ ʮ3ͰμΠΤοτʯ͠Α͏ͱࢥ͚ͬͨͲɺͣ΅Βͳࣗʹ͍͍ͯͳ͍ͷͰɺΊͨɻ
ִिPS݄ఔͰɺΧϥμεΩϟϯɻ
ه༰ ΧϥμεΩϟϯଌఆ߲ ମॏɺମࢷͳͲ τϨʔχϯά༰ ৯ࣄʢ֎৯ͷ߹ʣ
;Γ͔͑Γ
;Γ͔͑Γ ΠςϨʔγϣϯຖͷ࠷ޙʹ;Γ͔͑Γ ;Γ͔͑Γͱɺ1%$"αΠΫϧͷʮ$" 1 ʯͷ ෦ʹ֘͢ΔɻΠςϨʔγϣϯ͝ͱʹɺ1%$"Λ ·Θͯ͠ΈΑ͏ɻ ͪͳΈʹɺࠓճͷΧϥμվܭըͷୈΠςϨʔγϣ ϯͪΐ͏Ͳࠓࠒऴྃ͢Δɻ
;Γ͔͑Γݕ౼ࣄ߲ ,(*,1*͕ୡ͞Ε͍ͯΔ͔ɺݟ͕͠ඞཁ͔ ܭըͷݟ͕͠ඞཁ͔ ଥڠҊͷݟ͕͠ඞཁ͔ ΧϥμʹมԽ͕͋Δ͔ ΠςϨʔγϣϯظؒΛ͘PS͖͔͘͢ ! มԽʹؾ͚ͮɺͦΕ͕;Γ͔͑Γͷωλʹͭͳ͕Δɻ ଞਓΛר͖ࠐΊɺࣗͰؾ͔ͮͳ͍͜ͱࢦఠͯ͘͠ΕΔʢ͔ʣ
,15ͯ͠ΈΔ ;Γ͔͑ΓͷࡍʹɺΑ͘ΘΕΔϑΥʔϚοτ ,FFQ1SPCMFN5SZ
,15ͷϑΥʔϚοτ ,FFQ1SPCMFN5SZͷදʹ هड़ͨ͠ΓɺᝦΛష͚ͨ Γ͢Δͷ͕Ұൠత ϚΠϯυϚοϓΛ͏ํ๏ ͋Δ ,FFQ 1SPCMFN 5SZ
,15ͷΓํ ࠷ॳʹ,FFQͱ1SPCMFN͚ͩᝦʹॻ͍ͯషΓ͚ ,FFQͱ1SPCMFNΛͱʹɺ࣍ͷΠςϨʔγϣϯʹࢼ͍ͨ͜͠ͱΛ 5SZͱͯ͠ᝦʹॻ͍ͯషΓ͚ ,FFQΛΑΓൃలͤ͞Δͷ 1SPCMFNΛվળͤ͞Δͷ ͦΕҎ֎ʹͬͯΈ͍ͨ͜ͱ 5SZͷத͔Βɺ࣮ࡍʹ࣍ͷΠςϨʔγϣϯʹ࣮ࢪ͢ΔͷΛϐοΫ Ξοϓ
·ͱΊ
·ͱΊ ͜Ε͔ΒؒͷܹಆΛ͓͜ͳ͍·͢ɻ ·ͩୈΠςϨʔγϣϯͰ͢ɻ͜Ε͔Βܧଓ͠ͳ͚ΕͳΓ·ͤΜɻ ͳΜ͔ͩΜͩͰɺγεςϜ։ൃܭըͱΧϥμվܭըɺجຊతʹҰॹʂ γεςϜ։ൃܭըɺΧϥμվܭըҎ֎ͰԠ༻Ͱ͖Δͱࢥ͍·͢ɻ ֶशɺϚʔέςΟϯάɺFUD ଞͷ/%4ͷൃදΛ͖͔͚ͬʹͯ͠ɺࣗͳΓʹԠ༻ͯ͠ΈΔͷ໘ന͍ɻ ποίϛ͋Εɺ͍ͩ͘͞ɻվળ͍͖ͯ͠·͢ɻ ຊͷ࠙ձͰɺී௨ʹҿΈ৯͍͠·͢ɻΑΖ͘͠ʂ
͓·͚
৽ׁγςΟϚϥιϯ LN ΤϯτϦʔ͠·ͨ͠
ୈΠςϨʔγϣϯ ແʹLN͕ લ͠ʹͳΓ·͢
͝ਗ਼ௌ ͋Γ͕ͱ͏͍͟͝·ͨ͠