SlideShare a Scribd company logo
1 of 85
Download to read offline
ABEJA Provides AI Platform
Faster circulation of
high-volume data using
the ABEJA Platform
Data Collection Data Storage
Training
Deploying
Infering &
Retraining
AI-Ops for your business
ML
•
•
•
•
•
•
•
•
•
•
•
•
. - / - -
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Þ
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
. 1/ . 7 ./ 0 ./0: 1 7/ / 1 : 7 / / - 7 /2 - / / - 7
0 6 .-0 35/ .2 1 .4 7:
•
•
•
•
•
•
•
•
•
•
•
•
•
- DMCHV
• S 3 H DMF MC 7H 1DH"1DH .P C SP HMF MM R RH M E P TH S K AID R CDRD RH M PJ G
R RGD DMRW" HVRG ---4 . MEDPDM D M -PRHEH H K 4MRDKKHFDM D
• C SK HL DR K Y D C M R MDDC M A SMCHMF"A VD , P HMHMF AID R K CDRD R P S HMF
MKW GSL M TDPHEH RH M P DDCHMF E RGD 4000 MEDPDM D M L SRDP TH H M MC RRDPM
PD FMHRH M
• C SK HL DR K P HMHMF AID R K CDRD R P HRG KH J S DPTH H M P DDCHMF E
RGD 4000 . MEDPDM D M . L SRDP H H M MC RRDPM :D FMHRH M *
• C SK HL DR K 0VRPDLD KH JHMF E P DEEH HDMR AID R MM R RH M P DDCHMF E RGD
4000 4MRDPM RH M K . MEDPDM D M . L SRDP H H M *
• 6 MWS GJ T 6 DMH DR K 7D PMHMF HMRDKKHFDMR CH K F E P A SMCHMF A V MM R RH M P DDCHMF E
RGD 4000 . MEDPDM D M . L SRDP H H M MC RRDPM :D FMHRH M
• SRR HM SW F MC 6PH RDM 2P SL M PDCH RHMF SEEH HDMR MM R RH M RPDMFRG E P HMRDP RHTD
E PDFP SMC DFLDMR RH M P DDCHMF E RGD 4000 4MRDPM RH M K . MEDPDM D M . L SRDP H H M
• 7HM SMF" H DR K 8H P ER , . LL M AID R HM MRDVR 0SP D M MEDPDM D M L SRDP
TH H M PHMFDP .G L
0PPFNE W
• 1FB BN 0 X FS BL IBS R SIF PO NS F BNS D RFH FNSBS ON V SI PO NS R PF R ON 4 OPFBN
DONGF FNDF ON DO P SF R ON P NHF 2IB ( +
• 2BRS F ON 9L R FS BL 0NNOSBS NH OC FDS NRSBNDFR V SI B POLXHON NN ODFFE NHR OG SIF 8444
2ONGF FNDF ON 2O P SF R ON BNE BSSF N FDOHN S ON ( ,
• 0D NB 3B E FS BL 4GG D FNS NSF BDS F BNNOSBS ON OG RFH FNSBS ON EBSBRFSR V SI POLXHON NN""
ODFFE NHR OG SIF 8444 2ONGF FNDF ON 2O P SF R ON BNE BSSF N FDOHN S ON ( -
• 9 NH 7 BN FS BL BRS 8NSF BDS F C FDS 0NNOSBS ON V SI 2 F 62: B A P FP NS
B A . ) +-, ( .

More Related Content

More from Takanori Ogata

20210108 Tread: Circuits
20210108 Tread: Circuits20210108 Tread: Circuits
20210108 Tread: CircuitsTakanori Ogata
 
20200704 Deep Snake for Real-Time Instance Segmentation
20200704 Deep Snake for Real-Time Instance Segmentation 20200704 Deep Snake for Real-Time Instance Segmentation
20200704 Deep Snake for Real-Time Instance Segmentation Takanori Ogata
 
CVPR2019読み会@関東CV
CVPR2019読み会@関東CVCVPR2019読み会@関東CV
CVPR2019読み会@関東CVTakanori Ogata
 
180204 Attention-aware Deep Reinforcement Learning for Video Face Recognition
180204 Attention-aware Deep Reinforcement Learning for Video Face Recognition180204 Attention-aware Deep Reinforcement Learning for Video Face Recognition
180204 Attention-aware Deep Reinforcement Learning for Video Face RecognitionTakanori Ogata
 
Unsupervised learning of object landmarks by factorized spatial embeddings
Unsupervised learning of object landmarks by factorized spatial embeddingsUnsupervised learning of object landmarks by factorized spatial embeddings
Unsupervised learning of object landmarks by factorized spatial embeddingsTakanori Ogata
 
Annotating object instances with a polygon rnn
Annotating object instances with a polygon rnnAnnotating object instances with a polygon rnn
Annotating object instances with a polygon rnnTakanori Ogata
 
Training object class detectors with click supervision
Training object class detectors with click supervisionTraining object class detectors with click supervision
Training object class detectors with click supervisionTakanori Ogata
 
SSD: Single Shot MultiBox Detector (ECCV2016)
SSD: Single Shot MultiBox Detector (ECCV2016)SSD: Single Shot MultiBox Detector (ECCV2016)
SSD: Single Shot MultiBox Detector (ECCV2016)Takanori Ogata
 
160924 Deep Learning Tuningathon
160924 Deep Learning Tuningathon160924 Deep Learning Tuningathon
160924 Deep Learning TuningathonTakanori Ogata
 
Convolutional Pose Machines
Convolutional Pose MachinesConvolutional Pose Machines
Convolutional Pose MachinesTakanori Ogata
 
Deep Learningライブラリ 色々つかってみた感想まとめ
Deep Learningライブラリ 色々つかってみた感想まとめDeep Learningライブラリ 色々つかってみた感想まとめ
Deep Learningライブラリ 色々つかってみた感想まとめTakanori Ogata
 
10分でわかる主成分分析(PCA)
10分でわかる主成分分析(PCA)10分でわかる主成分分析(PCA)
10分でわかる主成分分析(PCA)Takanori Ogata
 

More from Takanori Ogata (15)

20210108 Tread: Circuits
20210108 Tread: Circuits20210108 Tread: Circuits
20210108 Tread: Circuits
 
20200704 Deep Snake for Real-Time Instance Segmentation
20200704 Deep Snake for Real-Time Instance Segmentation 20200704 Deep Snake for Real-Time Instance Segmentation
20200704 Deep Snake for Real-Time Instance Segmentation
 
CVPR2019読み会@関東CV
CVPR2019読み会@関東CVCVPR2019読み会@関東CV
CVPR2019読み会@関東CV
 
190410 ML@LOFT
190410 ML@LOFT190410 ML@LOFT
190410 ML@LOFT
 
180204 Attention-aware Deep Reinforcement Learning for Video Face Recognition
180204 Attention-aware Deep Reinforcement Learning for Video Face Recognition180204 Attention-aware Deep Reinforcement Learning for Video Face Recognition
180204 Attention-aware Deep Reinforcement Learning for Video Face Recognition
 
Unsupervised learning of object landmarks by factorized spatial embeddings
Unsupervised learning of object landmarks by factorized spatial embeddingsUnsupervised learning of object landmarks by factorized spatial embeddings
Unsupervised learning of object landmarks by factorized spatial embeddings
 
Annotating object instances with a polygon rnn
Annotating object instances with a polygon rnnAnnotating object instances with a polygon rnn
Annotating object instances with a polygon rnn
 
Training object class detectors with click supervision
Training object class detectors with click supervisionTraining object class detectors with click supervision
Training object class detectors with click supervision
 
SSD: Single Shot MultiBox Detector (ECCV2016)
SSD: Single Shot MultiBox Detector (ECCV2016)SSD: Single Shot MultiBox Detector (ECCV2016)
SSD: Single Shot MultiBox Detector (ECCV2016)
 
160924 Deep Learning Tuningathon
160924 Deep Learning Tuningathon160924 Deep Learning Tuningathon
160924 Deep Learning Tuningathon
 
Convolutional Pose Machines
Convolutional Pose MachinesConvolutional Pose Machines
Convolutional Pose Machines
 
Deep Learningライブラリ 色々つかってみた感想まとめ
Deep Learningライブラリ 色々つかってみた感想まとめDeep Learningライブラリ 色々つかってみた感想まとめ
Deep Learningライブラリ 色々つかってみた感想まとめ
 
Cv20160205
Cv20160205Cv20160205
Cv20160205
 
10分でわかる主成分分析(PCA)
10分でわかる主成分分析(PCA)10分でわかる主成分分析(PCA)
10分でわかる主成分分析(PCA)
 
DeepAKB
DeepAKBDeepAKB
DeepAKB
 

Recently uploaded

New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 

Recently uploaded (20)

New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 

ABEJA AI Platform Provides Fast Data Circulation for High-Volume Data

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. ABEJA Provides AI Platform Faster circulation of high-volume data using the ABEJA Platform Data Collection Data Storage Training Deploying Infering & Retraining
  • 7. AI-Ops for your business
  • 8. ML
  • 10.
  • 11.
  • 12.
  • 13.
  • 14. . - / - -
  • 15.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 25.
  • 26.
  • 27.
  • 28.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53.
  • 55.
  • 56.
  • 57.
  • 59.
  • 60.
  • 61.
  • 63.
  • 64.
  • 66.
  • 67. . 1/ . 7 ./ 0 ./0: 1 7/ / 1 : 7 / / - 7 /2 - / / - 7
  • 68. 0 6 .-0 35/ .2 1 .4 7:
  • 70.
  • 71.
  • 75.
  • 76.
  • 77.
  • 79.
  • 80.
  • 81.
  • 82.
  • 83.
  • 84. - DMCHV • S 3 H DMF MC 7H 1DH"1DH .P C SP HMF MM R RH M E P TH S K AID R CDRD RH M PJ G R RGD DMRW" HVRG ---4 . MEDPDM D M -PRHEH H K 4MRDKKHFDM D • C SK HL DR K Y D C M R MDDC M A SMCHMF"A VD , P HMHMF AID R K CDRD R P S HMF MKW GSL M TDPHEH RH M P DDCHMF E RGD 4000 MEDPDM D M L SRDP TH H M MC RRDPM PD FMHRH M • C SK HL DR K P HMHMF AID R K CDRD R P HRG KH J S DPTH H M P DDCHMF E RGD 4000 . MEDPDM D M . L SRDP H H M MC RRDPM :D FMHRH M * • C SK HL DR K 0VRPDLD KH JHMF E P DEEH HDMR AID R MM R RH M P DDCHMF E RGD 4000 4MRDPM RH M K . MEDPDM D M . L SRDP H H M * • 6 MWS GJ T 6 DMH DR K 7D PMHMF HMRDKKHFDMR CH K F E P A SMCHMF A V MM R RH M P DDCHMF E RGD 4000 . MEDPDM D M . L SRDP H H M MC RRDPM :D FMHRH M • SRR HM SW F MC 6PH RDM 2P SL M PDCH RHMF SEEH HDMR MM R RH M RPDMFRG E P HMRDP RHTD E PDFP SMC DFLDMR RH M P DDCHMF E RGD 4000 4MRDPM RH M K . MEDPDM D M . L SRDP H H M • 7HM SMF" H DR K 8H P ER , . LL M AID R HM MRDVR 0SP D M MEDPDM D M L SRDP TH H M PHMFDP .G L
  • 85. 0PPFNE W • 1FB BN 0 X FS BL IBS R SIF PO NS F BNS D RFH FNSBS ON V SI PO NS R PF R ON 4 OPFBN DONGF FNDF ON DO P SF R ON P NHF 2IB ( + • 2BRS F ON 9L R FS BL 0NNOSBS NH OC FDS NRSBNDFR V SI B POLXHON NN ODFFE NHR OG SIF 8444 2ONGF FNDF ON 2O P SF R ON BNE BSSF N FDOHN S ON ( , • 0D NB 3B E FS BL 4GG D FNS NSF BDS F BNNOSBS ON OG RFH FNSBS ON EBSBRFSR V SI POLXHON NN"" ODFFE NHR OG SIF 8444 2ONGF FNDF ON 2O P SF R ON BNE BSSF N FDOHN S ON ( - • 9 NH 7 BN FS BL BRS 8NSF BDS F C FDS 0NNOSBS ON V SI 2 F 62: B A P FP NS B A . ) +-, ( .