Soumettre la recherche
Mettre en ligne
Machine Learning with Amazon SageMaker
•
0 j'aime
•
282 vues
Vladimir Simek
Suivre
Strojové účenie s Amazon SageMaker-om. AWS Česko-Slovenský webinár, odvysielané 09/02/2021
Lire moins
Lire la suite
Technologie
Signaler
Partager
Signaler
Partager
1 sur 37
Télécharger maintenant
Télécharger pour lire hors ligne
Recommandé
Machine Learning & Amazon SageMaker
Machine Learning & Amazon SageMaker
Amazon Web Services
Introducing Amazon SageMaker
Introducing Amazon SageMaker
Amazon Web Services
End-to-End Machine Learning with Amazon SageMaker
End-to-End Machine Learning with Amazon SageMaker
Sungmin Kim
Introduction to Sagemaker
Introduction to Sagemaker
Amazon Web Services
Amazon SageMaker 모델 학습 방법 소개::최영준, 솔루션즈 아키텍트 AI/ML 엑스퍼트, AWS::AWS AIML 스페셜 웨비나
Amazon SageMaker 모델 학습 방법 소개::최영준, 솔루션즈 아키텍트 AI/ML 엑스퍼트, AWS::AWS AIML 스페셜 웨비나
Amazon Web Services Korea
Intro to SageMaker
Intro to SageMaker
Soji Adeshina
Amazon SageMaker
Amazon SageMaker
Amazon Web Services
Speed up data preparation for ML pipelines on AWS
Speed up data preparation for ML pipelines on AWS
Data Science Milan
Recommandé
Machine Learning & Amazon SageMaker
Machine Learning & Amazon SageMaker
Amazon Web Services
Introducing Amazon SageMaker
Introducing Amazon SageMaker
Amazon Web Services
End-to-End Machine Learning with Amazon SageMaker
End-to-End Machine Learning with Amazon SageMaker
Sungmin Kim
Introduction to Sagemaker
Introduction to Sagemaker
Amazon Web Services
Amazon SageMaker 모델 학습 방법 소개::최영준, 솔루션즈 아키텍트 AI/ML 엑스퍼트, AWS::AWS AIML 스페셜 웨비나
Amazon SageMaker 모델 학습 방법 소개::최영준, 솔루션즈 아키텍트 AI/ML 엑스퍼트, AWS::AWS AIML 스페셜 웨비나
Amazon Web Services Korea
Intro to SageMaker
Intro to SageMaker
Soji Adeshina
Amazon SageMaker
Amazon SageMaker
Amazon Web Services
Speed up data preparation for ML pipelines on AWS
Speed up data preparation for ML pipelines on AWS
Data Science Milan
Ml ops on AWS
Ml ops on AWS
PhilipBasford
Amazon SageMaker 모델 빌딩 파이프라인 소개::이유동, AI/ML 스페셜리스트 솔루션즈 아키텍트, AWS::AWS AIML 스...
Amazon SageMaker 모델 빌딩 파이프라인 소개::이유동, AI/ML 스페셜리스트 솔루션즈 아키텍트, AWS::AWS AIML 스...
Amazon Web Services Korea
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
Provectus
AWS를 활용한 리테일,이커머스 워크로드와 온라인 서비스 이관 사례::이동열, 임혁용:: AWS Summit Seoul 2018
AWS를 활용한 리테일,이커머스 워크로드와 온라인 서비스 이관 사례::이동열, 임혁용:: AWS Summit Seoul 2018
Amazon Web Services Korea
AWS 101: Introduction to AWS
AWS 101: Introduction to AWS
Ian Massingham
Databricks Overview for MLOps
Databricks Overview for MLOps
Databricks
Azure AI platform - Automated ML workshop
Azure AI platform - Automated ML workshop
Parashar Shah
MLops workshop AWS
MLops workshop AWS
Gili Nachum
Introduction to Amazon Kinesis Firehose - AWS August Webinar Series
Introduction to Amazon Kinesis Firehose - AWS August Webinar Series
Amazon Web Services
Building a Modern Data Architecture on AWS - Webinar
Building a Modern Data Architecture on AWS - Webinar
Amazon Web Services
AWS Summit Seoul 2023 | 생성 AI 모델의 임베딩 벡터를 이용한 서버리스 추천 검색 구현하기
AWS Summit Seoul 2023 | 생성 AI 모델의 임베딩 벡터를 이용한 서버리스 추천 검색 구현하기
Amazon Web Services Korea
[Bespin Global 파트너 세션] 분산 데이터 통합 (Data Lake) 기반의 데이터 분석 환경 구축 사례 - 베스핀 글로벌 장익...
[Bespin Global 파트너 세션] 분산 데이터 통합 (Data Lake) 기반의 데이터 분석 환경 구축 사례 - 베스핀 글로벌 장익...
Amazon Web Services Korea
민첩하고 비용효율적인 Data Lake 구축 - 문종민 솔루션즈 아키텍트, AWS
민첩하고 비용효율적인 Data Lake 구축 - 문종민 솔루션즈 아키텍트, AWS
Amazon Web Services Korea
(ISM208) The Science of Saving with AWS Reserved Instances
(ISM208) The Science of Saving with AWS Reserved Instances
Amazon Web Services
Google Vertex AI
Google Vertex AI
VikasBisoi
Building-a-Data-Lake-on-AWS
Building-a-Data-Lake-on-AWS
Amazon Web Services
Cloud Migration Workshop
Cloud Migration Workshop
Amazon Web Services
AWS Summit Seoul 2023 | 천만 사용자 서비스를 위한 Amazon SageMaker 활용 방법 진화하기
AWS Summit Seoul 2023 | 천만 사용자 서비스를 위한 Amazon SageMaker 활용 방법 진화하기
Amazon Web Services Korea
Cost Optimisation on AWS
Cost Optimisation on AWS
Amazon Web Services
Cost Optimisation on AWS
Cost Optimisation on AWS
Amazon Web Services
How to train and deploy your machine learning models with Amazon SageMaker
How to train and deploy your machine learning models with Amazon SageMaker
Amazon Web Services
[AWS Innovate 온라인 컨퍼런스] Kubernetes와 SageMaker를 활용하여 Machine Learning 워크로드 관리하...
[AWS Innovate 온라인 컨퍼런스] Kubernetes와 SageMaker를 활용하여 Machine Learning 워크로드 관리하...
Amazon Web Services Korea
Contenu connexe
Tendances
Ml ops on AWS
Ml ops on AWS
PhilipBasford
Amazon SageMaker 모델 빌딩 파이프라인 소개::이유동, AI/ML 스페셜리스트 솔루션즈 아키텍트, AWS::AWS AIML 스...
Amazon SageMaker 모델 빌딩 파이프라인 소개::이유동, AI/ML 스페셜리스트 솔루션즈 아키텍트, AWS::AWS AIML 스...
Amazon Web Services Korea
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
Provectus
AWS를 활용한 리테일,이커머스 워크로드와 온라인 서비스 이관 사례::이동열, 임혁용:: AWS Summit Seoul 2018
AWS를 활용한 리테일,이커머스 워크로드와 온라인 서비스 이관 사례::이동열, 임혁용:: AWS Summit Seoul 2018
Amazon Web Services Korea
AWS 101: Introduction to AWS
AWS 101: Introduction to AWS
Ian Massingham
Databricks Overview for MLOps
Databricks Overview for MLOps
Databricks
Azure AI platform - Automated ML workshop
Azure AI platform - Automated ML workshop
Parashar Shah
MLops workshop AWS
MLops workshop AWS
Gili Nachum
Introduction to Amazon Kinesis Firehose - AWS August Webinar Series
Introduction to Amazon Kinesis Firehose - AWS August Webinar Series
Amazon Web Services
Building a Modern Data Architecture on AWS - Webinar
Building a Modern Data Architecture on AWS - Webinar
Amazon Web Services
AWS Summit Seoul 2023 | 생성 AI 모델의 임베딩 벡터를 이용한 서버리스 추천 검색 구현하기
AWS Summit Seoul 2023 | 생성 AI 모델의 임베딩 벡터를 이용한 서버리스 추천 검색 구현하기
Amazon Web Services Korea
[Bespin Global 파트너 세션] 분산 데이터 통합 (Data Lake) 기반의 데이터 분석 환경 구축 사례 - 베스핀 글로벌 장익...
[Bespin Global 파트너 세션] 분산 데이터 통합 (Data Lake) 기반의 데이터 분석 환경 구축 사례 - 베스핀 글로벌 장익...
Amazon Web Services Korea
민첩하고 비용효율적인 Data Lake 구축 - 문종민 솔루션즈 아키텍트, AWS
민첩하고 비용효율적인 Data Lake 구축 - 문종민 솔루션즈 아키텍트, AWS
Amazon Web Services Korea
(ISM208) The Science of Saving with AWS Reserved Instances
(ISM208) The Science of Saving with AWS Reserved Instances
Amazon Web Services
Google Vertex AI
Google Vertex AI
VikasBisoi
Building-a-Data-Lake-on-AWS
Building-a-Data-Lake-on-AWS
Amazon Web Services
Cloud Migration Workshop
Cloud Migration Workshop
Amazon Web Services
AWS Summit Seoul 2023 | 천만 사용자 서비스를 위한 Amazon SageMaker 활용 방법 진화하기
AWS Summit Seoul 2023 | 천만 사용자 서비스를 위한 Amazon SageMaker 활용 방법 진화하기
Amazon Web Services Korea
Cost Optimisation on AWS
Cost Optimisation on AWS
Amazon Web Services
Cost Optimisation on AWS
Cost Optimisation on AWS
Amazon Web Services
Tendances
(20)
Ml ops on AWS
Ml ops on AWS
Amazon SageMaker 모델 빌딩 파이프라인 소개::이유동, AI/ML 스페셜리스트 솔루션즈 아키텍트, AWS::AWS AIML 스...
Amazon SageMaker 모델 빌딩 파이프라인 소개::이유동, AI/ML 스페셜리스트 솔루션즈 아키텍트, AWS::AWS AIML 스...
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
AWS를 활용한 리테일,이커머스 워크로드와 온라인 서비스 이관 사례::이동열, 임혁용:: AWS Summit Seoul 2018
AWS를 활용한 리테일,이커머스 워크로드와 온라인 서비스 이관 사례::이동열, 임혁용:: AWS Summit Seoul 2018
AWS 101: Introduction to AWS
AWS 101: Introduction to AWS
Databricks Overview for MLOps
Databricks Overview for MLOps
Azure AI platform - Automated ML workshop
Azure AI platform - Automated ML workshop
MLops workshop AWS
MLops workshop AWS
Introduction to Amazon Kinesis Firehose - AWS August Webinar Series
Introduction to Amazon Kinesis Firehose - AWS August Webinar Series
Building a Modern Data Architecture on AWS - Webinar
Building a Modern Data Architecture on AWS - Webinar
AWS Summit Seoul 2023 | 생성 AI 모델의 임베딩 벡터를 이용한 서버리스 추천 검색 구현하기
AWS Summit Seoul 2023 | 생성 AI 모델의 임베딩 벡터를 이용한 서버리스 추천 검색 구현하기
[Bespin Global 파트너 세션] 분산 데이터 통합 (Data Lake) 기반의 데이터 분석 환경 구축 사례 - 베스핀 글로벌 장익...
[Bespin Global 파트너 세션] 분산 데이터 통합 (Data Lake) 기반의 데이터 분석 환경 구축 사례 - 베스핀 글로벌 장익...
민첩하고 비용효율적인 Data Lake 구축 - 문종민 솔루션즈 아키텍트, AWS
민첩하고 비용효율적인 Data Lake 구축 - 문종민 솔루션즈 아키텍트, AWS
(ISM208) The Science of Saving with AWS Reserved Instances
(ISM208) The Science of Saving with AWS Reserved Instances
Google Vertex AI
Google Vertex AI
Building-a-Data-Lake-on-AWS
Building-a-Data-Lake-on-AWS
Cloud Migration Workshop
Cloud Migration Workshop
AWS Summit Seoul 2023 | 천만 사용자 서비스를 위한 Amazon SageMaker 활용 방법 진화하기
AWS Summit Seoul 2023 | 천만 사용자 서비스를 위한 Amazon SageMaker 활용 방법 진화하기
Cost Optimisation on AWS
Cost Optimisation on AWS
Cost Optimisation on AWS
Cost Optimisation on AWS
Similaire à Machine Learning with Amazon SageMaker
How to train and deploy your machine learning models with Amazon SageMaker
How to train and deploy your machine learning models with Amazon SageMaker
Amazon Web Services
[AWS Innovate 온라인 컨퍼런스] Kubernetes와 SageMaker를 활용하여 Machine Learning 워크로드 관리하...
[AWS Innovate 온라인 컨퍼런스] Kubernetes와 SageMaker를 활용하여 Machine Learning 워크로드 관리하...
Amazon Web Services Korea
Easily Label Training Data For Machine Learning At Scale.pptx
Easily Label Training Data For Machine Learning At Scale.pptx
Neel688696
Easily Label Training Data For Machine Learning At Scale.pptx
Easily Label Training Data For Machine Learning At Scale.pptx
Neel688696
Integrate Machine Learning into Your Spring Application in Less than an Hour
Integrate Machine Learning into Your Spring Application in Less than an Hour
VMware Tanzu
AWS reinvent 2019 recap - Riyadh - AI And ML - Ahmed Raafat
AWS reinvent 2019 recap - Riyadh - AI And ML - Ahmed Raafat
AWS Riyadh User Group
Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sage...
Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sage...
Amazon Web Services
Sviluppa, addestra e distribuisci modelli di Machine learning su qualsiasi scala
Sviluppa, addestra e distribuisci modelli di Machine learning su qualsiasi scala
Amazon Web Services
Deep Dive Amazon SageMaker
Deep Dive Amazon SageMaker
Cobus Bernard
Train ML Models Using Amazon SageMaker with TensorFlow - SRV336 - Chicago AWS...
Train ML Models Using Amazon SageMaker with TensorFlow - SRV336 - Chicago AWS...
Amazon Web Services
Supercharge your Machine Learning Solutions with Amazon SageMaker
Supercharge your Machine Learning Solutions with Amazon SageMaker
Amazon Web Services
Amazon SageMaker Build, Train and Deploy Your ML Models
Amazon SageMaker Build, Train and Deploy Your ML Models
AWS Riyadh User Group
Supercharge Your Machine Learning Solutions with Amazon SageMaker
Supercharge Your Machine Learning Solutions with Amazon SageMaker
Amazon Web Services
Build, train and deploy ML models with SageMaker (October 2019)
Build, train and deploy ML models with SageMaker (October 2019)
Julien SIMON
Machine Learning in azione con Amazon SageMaker
Machine Learning in azione con Amazon SageMaker
Amazon Web Services
Amazon SageMaker workshop
Amazon SageMaker workshop
Julien SIMON
Train & Deploy ML Models with Amazon Sagemaker: Collision 2018
Train & Deploy ML Models with Amazon Sagemaker: Collision 2018
Amazon Web Services
Amazon reInvent 2020 Recap: AI and Machine Learning
Amazon reInvent 2020 Recap: AI and Machine Learning
Chris Fregly
Machine Learning: From Notebook to Production with Amazon Sagemaker (April 2018)
Machine Learning: From Notebook to Production with Amazon Sagemaker (April 2018)
Julien SIMON
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta AWS Summit
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta AWS Summit
Amazon Web Services
Similaire à Machine Learning with Amazon SageMaker
(20)
How to train and deploy your machine learning models with Amazon SageMaker
How to train and deploy your machine learning models with Amazon SageMaker
[AWS Innovate 온라인 컨퍼런스] Kubernetes와 SageMaker를 활용하여 Machine Learning 워크로드 관리하...
[AWS Innovate 온라인 컨퍼런스] Kubernetes와 SageMaker를 활용하여 Machine Learning 워크로드 관리하...
Easily Label Training Data For Machine Learning At Scale.pptx
Easily Label Training Data For Machine Learning At Scale.pptx
Easily Label Training Data For Machine Learning At Scale.pptx
Easily Label Training Data For Machine Learning At Scale.pptx
Integrate Machine Learning into Your Spring Application in Less than an Hour
Integrate Machine Learning into Your Spring Application in Less than an Hour
AWS reinvent 2019 recap - Riyadh - AI And ML - Ahmed Raafat
AWS reinvent 2019 recap - Riyadh - AI And ML - Ahmed Raafat
Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sage...
Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sage...
Sviluppa, addestra e distribuisci modelli di Machine learning su qualsiasi scala
Sviluppa, addestra e distribuisci modelli di Machine learning su qualsiasi scala
Deep Dive Amazon SageMaker
Deep Dive Amazon SageMaker
Train ML Models Using Amazon SageMaker with TensorFlow - SRV336 - Chicago AWS...
Train ML Models Using Amazon SageMaker with TensorFlow - SRV336 - Chicago AWS...
Supercharge your Machine Learning Solutions with Amazon SageMaker
Supercharge your Machine Learning Solutions with Amazon SageMaker
Amazon SageMaker Build, Train and Deploy Your ML Models
Amazon SageMaker Build, Train and Deploy Your ML Models
Supercharge Your Machine Learning Solutions with Amazon SageMaker
Supercharge Your Machine Learning Solutions with Amazon SageMaker
Build, train and deploy ML models with SageMaker (October 2019)
Build, train and deploy ML models with SageMaker (October 2019)
Machine Learning in azione con Amazon SageMaker
Machine Learning in azione con Amazon SageMaker
Amazon SageMaker workshop
Amazon SageMaker workshop
Train & Deploy ML Models with Amazon Sagemaker: Collision 2018
Train & Deploy ML Models with Amazon Sagemaker: Collision 2018
Amazon reInvent 2020 Recap: AI and Machine Learning
Amazon reInvent 2020 Recap: AI and Machine Learning
Machine Learning: From Notebook to Production with Amazon Sagemaker (April 2018)
Machine Learning: From Notebook to Production with Amazon Sagemaker (April 2018)
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta AWS Summit
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta AWS Summit
Plus de Vladimir Simek
AWS CZSK Webinář 2020.03: AWS Outposts
AWS CZSK Webinář 2020.03: AWS Outposts
Vladimir Simek
AWS CZSK Webinar - Migrácia desktopov a aplikácií do AWS cloudu s Amazon Work...
AWS CZSK Webinar - Migrácia desktopov a aplikácií do AWS cloudu s Amazon Work...
Vladimir Simek
News from re:Invent 2019
News from re:Invent 2019
Vladimir Simek
Serverless on AWS: Architectural Patterns and Best Practices
Serverless on AWS: Architectural Patterns and Best Practices
Vladimir Simek
AWS CZSK Webinar 2019.07: Databazy na AWS
AWS CZSK Webinar 2019.07: Databazy na AWS
Vladimir Simek
AWS CZSK Webinář 2019.05: Jak chránit vaše webové aplikace před DDoS útoky
AWS CZSK Webinář 2019.05: Jak chránit vaše webové aplikace před DDoS útoky
Vladimir Simek
Česko-Slovenský AWS Webinář 07 - Optimalizace nákladů v AWS
Česko-Slovenský AWS Webinář 07 - Optimalizace nákladů v AWS
Vladimir Simek
AWS Česko-Slovenský Webinár 03: Vývoj v AWS
AWS Česko-Slovenský Webinár 03: Vývoj v AWS
Vladimir Simek
Gaming with AWS
Gaming with AWS
Vladimir Simek
Artificial Intelligence (Machine Learning) on AWS: How to Start
Artificial Intelligence (Machine Learning) on AWS: How to Start
Vladimir Simek
Artificial Intelligence (Machine Learning) on AWS: How to Start
Artificial Intelligence (Machine Learning) on AWS: How to Start
Vladimir Simek
AWS Webinar CZSK 02 Bezpecnost v AWS cloudu
AWS Webinar CZSK 02 Bezpecnost v AWS cloudu
Vladimir Simek
AWS Webinar CZSK Uvod do cloud computingu
AWS Webinar CZSK Uvod do cloud computingu
Vladimir Simek
Introduction to EKS (AWS User Group Slovakia)
Introduction to EKS (AWS User Group Slovakia)
Vladimir Simek
Running Docker Containers on AWS
Running Docker Containers on AWS
Vladimir Simek
Travel hackathon
Travel hackathon
Vladimir Simek
How to run your Hadoop Cluster in 10 minutes
How to run your Hadoop Cluster in 10 minutes
Vladimir Simek
CI&CD with AWS - AWS Prague User Group - May 2015
CI&CD with AWS - AWS Prague User Group - May 2015
Vladimir Simek
Plus de Vladimir Simek
(18)
AWS CZSK Webinář 2020.03: AWS Outposts
AWS CZSK Webinář 2020.03: AWS Outposts
AWS CZSK Webinar - Migrácia desktopov a aplikácií do AWS cloudu s Amazon Work...
AWS CZSK Webinar - Migrácia desktopov a aplikácií do AWS cloudu s Amazon Work...
News from re:Invent 2019
News from re:Invent 2019
Serverless on AWS: Architectural Patterns and Best Practices
Serverless on AWS: Architectural Patterns and Best Practices
AWS CZSK Webinar 2019.07: Databazy na AWS
AWS CZSK Webinar 2019.07: Databazy na AWS
AWS CZSK Webinář 2019.05: Jak chránit vaše webové aplikace před DDoS útoky
AWS CZSK Webinář 2019.05: Jak chránit vaše webové aplikace před DDoS útoky
Česko-Slovenský AWS Webinář 07 - Optimalizace nákladů v AWS
Česko-Slovenský AWS Webinář 07 - Optimalizace nákladů v AWS
AWS Česko-Slovenský Webinár 03: Vývoj v AWS
AWS Česko-Slovenský Webinár 03: Vývoj v AWS
Gaming with AWS
Gaming with AWS
Artificial Intelligence (Machine Learning) on AWS: How to Start
Artificial Intelligence (Machine Learning) on AWS: How to Start
Artificial Intelligence (Machine Learning) on AWS: How to Start
Artificial Intelligence (Machine Learning) on AWS: How to Start
AWS Webinar CZSK 02 Bezpecnost v AWS cloudu
AWS Webinar CZSK 02 Bezpecnost v AWS cloudu
AWS Webinar CZSK Uvod do cloud computingu
AWS Webinar CZSK Uvod do cloud computingu
Introduction to EKS (AWS User Group Slovakia)
Introduction to EKS (AWS User Group Slovakia)
Running Docker Containers on AWS
Running Docker Containers on AWS
Travel hackathon
Travel hackathon
How to run your Hadoop Cluster in 10 minutes
How to run your Hadoop Cluster in 10 minutes
CI&CD with AWS - AWS Prague User Group - May 2015
CI&CD with AWS - AWS Prague User Group - May 2015
Dernier
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
danishmna97
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
Sandro Moreira
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Angeliki Cooney
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Juan lago vázquez
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
apidays
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
Christopher Logan Kennedy
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
rafiqahmad00786416
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Zilliz
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
Product Anonymous
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
MadyBayot
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Edi Saputra
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
Zilliz
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Orbitshub
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
jfdjdjcjdnsjd
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Orbitshub
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
Dernier
(20)
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
Machine Learning with Amazon SageMaker
1.
1 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | 1 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Machine learning for every data scientist and developer Vladimír Šimek Sr. Solutions Architect, AWS Machine Learning with Amazon SageMaker AWS Česko-Slovenský Webinár 02/2021
2.
2 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Agenda • Gentle introduction • AI/ML on AWS – the full stack • Introduction to Amazon SageMaker • Use Cases • Demos • Resources • Q&A in chat window
3.
3 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | The AWS ML Stack Broadest and most complete set of machine learning capabilities Amazon SageMaker VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD CONTACT CENTERS Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Trainium Inferentia FPGA AI SERVICES ML SERVICES FRAMEWORKS & INFRASTRUCTURE DeepGraphLibrary Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Lex Amazon Personalize Amazon Forecast Amazon Comprehend +Medical Amazon Textract Amazon Kendra Amazon CodeGuru Amazon Fraud Detector Amazon Translate INDUSTRIAL AI CODE AND DEVOPS NEW Amazon DevOps Guru Voice ID For Amazon Connect Contact Lens NEW Amazon Monitron NEW AWS Panorama + Appliance NEW Amazon Lookout for Vision NEW Amazon Lookout for Equipment NEW Amazon HealthLake HEALTH AI NEW Amazon Lookout for Metrics ANOMALY DETECTION Amazon Transcribe for Medical Amazon Comprehend for Medical Label data NEW Aggregate & prepare data NEW Store & share features Auto ML Spark/R NEW Detect bias Visualize in notebooks Pick algorithm Train models Tune parameters NEW Debug & profile Deploy in production Manage & monitor NEW CI/CD Human review NEW: Model management for edge devices NEW: SageMaker JumpStart SAGEMAKER STUDIO IDE
4.
4 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Machine learning development is complex and costly Visualize in notebooks Pick algorithm Train models Tune parameters Deploy in production Manage and monitor Label data Collect and prepare data Store features CI/CD Check for bias
5.
5 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | https://aws.amazon.com/sagemaker Amazon SageMaker Most complete, end-to-end ML service Integrated Workbench Capabilities designed specifically for ML, data preparation, experiment management, and workflows Managed Infrastructure Designed for ultra low latency and high throughput, automatic scaling, and distributed training Managed Tooling Purpose-built from the ground up to work together including auto ML, collaboration, debugger, profiler, bias analyzer, and explainability
6.
6 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker overview PREPARE SageMaker Ground Truth Label training data for machine learning SageMaker Data Wrangler NEW Aggregate and prepare data for machine learning SageMaker Processing Built-in Python, BYO R/Spark SageMaker Feature Store NEW Store, update, retrieve, and share features SageMaker Clarify NEW Detect bias and understand model predictions BUILD SageMaker Studio Notebooks Jupyter notebooks with elastic compute and sharing Built-in and Bring your-own Algorithms Dozens of optimized algorithms or bring your own Local Mode Test and prototype on your local machine SageMaker Autopilot Automatically create machine learning models with full visibility SageMaker JumpStart NEW Pre-built solutions for common use cases TRAIN & TUNE Managed Training Distributed infrastructure management SageMaker Experiments Capture, organize, and compare every step Automatic Model Tuning Hyperparameter optimization Distributed Training Libraries NEW Training for large datasets and models SageMaker Debugger NEW Debug and profile training runs Managed Spot Training Reduce training cost by 90% DEPLOY & MANAGE Managed Deployment Fully managed, ultra low latency, high throughput Kubernetes & Kubeflow Integration Simplify Kubernetes-based machine learning Multi-Model Endpoints Reduce cost by hosting multiple models per instance SageMaker Model Monitor Maintain accuracy of deployed models SageMaker Edge Manager NEW Manage and monitor models on edge devices SageMaker Pipelines NEW Workflow orchestration and automation Amazon SageMaker SageMaker Studio Integrated development environment (IDE) for ML
7.
7 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Tens of thousands of customers use Amazon SageMaker
8.
8 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | SageMaker Common use cases Demand Forecasting Retail, Consumer Goods, Manufacturing Extract and Analyze Data from Documents Healthcare, Legal, Media/Ent, Education Computer Vision Healthcare, Pharma, Manufacturing Autonomous Driving Automotive, Transportation Personalized Recommendations Media & Entertainment, Retail, Education Churn Prediction Retail, Education, Software & Internet Predictive Maintenance Manufacturing, Automotive, IoT Fraud Detection Financial Services, Online Retail Credit Risk Prediction Financial Services, Retail
9.
9 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Features Amazon SageMaker Studio Amazon SageMaker Autopilot Amazon SageMaker JumpStart Amazon SageMaker Pipelines Amazon SageMaker Clarify PREPARE DATA Amazon SageMaker Ground Truth Amazon SageMaker Processing Amazon SageMaker Data Wrangler Amazon SageMaker Feature Store TRAIN Managed Training Amazon SageMaker Experiments Amazon SageMaker distributed training libraries Amazon SageMaker Debugger DEPLOY Managed Deployment Amazon SageMaker Edge Manager OTHER Kubernetes integration Security Human reviews
10.
10 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | 10 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Studio
11.
11 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Studio Fully Integrated Development Environment (IDE) for machine learning Collaboration at scale Share notebooks without tracking code dependencies Easy experiment management Organize, track, and compare thousands of experiments Automatic model generation Get accurate models with full visibility and control without writing code Higher quality ML models Automatically debug errors, monitor models, and maintain high quality Increased productivity Code, build, train, deploy, and monitor in a unified visual interface
12.
12 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Use Amazon SageMaker Studio to update models and see impact on model quality immediately
13.
13 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Notebooks Fast-start sharable notebooks Easy access with Single Sign-On (SSO) Access your notebooks in seconds Fully managed and secure Administrators manage access and permissions Fast setup Start your notebooks without spinning up compute resources Easy collaboration Share notebooks with a single click Flexible Dial up or down compute resources (coming soon)
14.
14 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Use Amazon SageMaker Notebooks to easily share your work with colleagues
15.
15 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Code dependencies are automatically captured to enable collaboration with colleagues
16.
16 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Autopilot Automatic model creation with full visibility and control Quick to start Provide your data in a tabular form and specify target prediction Automatic model creation Get ML models with feature engineering and model tuning automatically done Visibility and control Get notebooks for your models with source code Recommendations and optimization Get a leaderboard and continue to improve your model
17.
17 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Use Amazon SageMaker Autopilot to automatically train and tune the best machine learning models
18.
18 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | 18 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Demo #1
19.
19 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | 19 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker JumpStart
20.
20 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Getting started with machine learning can be challenging Requires knowledge of cloud infrastructure Time consuming Multiple steps involved with building, training, and deploying ML models
21.
21 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | SageMaker JumpStart Easily and quickly bring machine learning applications to market Solutions can be used out-of-the-box or can be customized for a specific business problem 15+ pre-built solutions for common ML use cases Use one-click deployable ML models and algorithms from popular model zoos Accelerate time to deploy over 150 open source models Easily bring ML applications to market using pre-built solutions, ML models, and algorithms from popular model zoos, and getting started content Get started with just a few clicks
22.
22 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | SageMaker JumpStart Use cases Autonomous driving Churn prediction Computer vision Credit risk prediction Demand forecasting Extract data from documents Fraud detection Personalized recommendations Predictive maintenance
23.
23 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | 23 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Data Wrangler
24.
24 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | 80% of time spent on data prep Source: Forbes survey of 80 data scientists, March 2016 60% 19% 9% 5% 4% 3% Cleaning and organizing data Collecting data sets Mining data for patterns Other Refining algorithms Building training sets What data scientists spend the most time doing
25.
25 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Data Wrangler The fastest and easiest way to prepare data for machine learning 25 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
26.
26 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | SageMaker Data Wrangler Use cases Cleanse & Explore Data Use built-in data transforms to accelerate data cleansing and exploration Visualize & Understand Data Enrich Data Quickly detect outliers or extreme values within a data set without the need to write code Use pre-configured data transformation tools to transform data into formats that can be used to build accurate ML models
27.
27 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Quickly select and query data Select data from Amazon Athena, Amazon Redshift, AWS Lake Formation, Amazon S3, and features from SageMaker Feature Store Write queries for data sources before importing data over to SageMaker Data Wrangler Import data in various file formats, such as CSV files, parquet files, and database tables directly into Amazon SageMaker
28.
28 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Easily transform data Transform your data without writing a single line of code using pre-configured data transforms Preconfigured data transforms include convert column type, rename column, and delete column Author custom transforms in PySpark, SQL, and Pandas Detect bias and identify dataset imbalance with SageMaker Clarify
29.
29 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Understand your data visually Intuitively understand your data with a set of pre-configured visualization templates Preconfigured visualization templates include histograms, scatter plots, box and whisker plots, line plots, and bar charts Interactively create and edit your own visualizations so you can quickly detect outliers or extreme values
30.
30 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Quickly estimate model accuracy Identify inconsistencies in data preparation workflows and diagnose issues before ML models are deployed into production Select subsets of data to identify errors Identify which features are contributing to model performance relative to others Determine if additional feature engineering is needed to improve model performance
31.
31 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Deploy data preparation workflows into production Export data preparation workflows to a notebook or Python code Integrate your workflow with SageMaker Pipelines to automate model deployment and management Publish created features to SageMaker Feature Store for reuse and syndication across teams and projects
32.
32 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | 32 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Demo #2
33.
33 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker is devops ready Security features to help you meet strict security requirements of ML workloads Security PCI, HIPAA, SOC 1/2/3, FedRAMP, and ISO 9001/27001/27017/27018 Compliance Create automated workflows in minutes to support thousands of models ML workflows Train complex models with massive datasets Scalability Automatic scheduling and execution of jobs with managed infrastructure Orchestration
34.
34 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker integrates with Kubernetes Amazon SageMaker Operators for Kubernetes 2 Amazon SageMaker Components for Kubeflow Pipelines 1 Pipelines
35.
35 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Built-in features help you go from idea to production faster, without compromising security Control data traffic across SageMaker components over a private network, and ensure appropriate ingress/egress with single-tenancy Infrastructure and network isolation Define, enforce, and audit who can be authenticated and authorized to use Amazon SageMaker resources Authentication and authorization Ensure automatic data encryption at rest and in transit with flexibility to bring your own keys Data protection Track, trace, and audit all API calls, events, data access, or interactions down to the user and IP level to ensure quick remediation Auditability and monitoring Inherit the most comprehensive compliance controls, and easily abide by your industry’s legislation Compliance certifications
36.
36 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | Resources https://aws.amazon.com/sagemaker/ https://aws.amazon.com/sagemaker/getting-started/ https://www.aws.training/ https://github.com/aws/amazon-sagemaker-examples https://www.getstartedonsagemaker.com/ https://aws.amazon.com/free/
37.
37 © 2020 Amazon
Web Services, Inc. or its affiliates. All rights reserved | 37 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Thank you vladsim@amazon.com
Télécharger maintenant