Soumettre la recherche
Mettre en ligne
Become a Machine Learning developer with AWS services (May 2019)
•
2 j'aime
•
861 vues
Julien SIMON
Suivre
Talk @ AWS Summit London with Lebara, 08/05/2019
Lire moins
Lire la suite
Technologie
Signaler
Partager
Signaler
Partager
1 sur 30
Télécharger maintenant
Télécharger pour lire hors ligne
Recommandé
Build, train and deploy ML models with Amazon SageMaker (May 2019)
Build, train and deploy ML models with Amazon SageMaker (May 2019)
Julien SIMON
Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)
Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)
Julien SIMON
Build, train and deploy ML models with SageMaker (October 2019)
Build, train and deploy ML models with SageMaker (October 2019)
Julien SIMON
Optimize your machine learning workloads on AWS (March 2019)
Optimize your machine learning workloads on AWS (March 2019)
Julien SIMON
Build, Train and Deploy Machine Learning Models at Scale (April 2019)
Build, Train and Deploy Machine Learning Models at Scale (April 2019)
Julien SIMON
A pragmatic introduction to natural language processing models (October 2019)
A pragmatic introduction to natural language processing models (October 2019)
Julien SIMON
Building Machine Learning Inference Pipelines at Scale (July 2019)
Building Machine Learning Inference Pipelines at Scale (July 2019)
Julien SIMON
Machine Learning on AWS (December 2018)
Machine Learning on AWS (December 2018)
Julien SIMON
Recommandé
Build, train and deploy ML models with Amazon SageMaker (May 2019)
Build, train and deploy ML models with Amazon SageMaker (May 2019)
Julien SIMON
Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)
Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)
Julien SIMON
Build, train and deploy ML models with SageMaker (October 2019)
Build, train and deploy ML models with SageMaker (October 2019)
Julien SIMON
Optimize your machine learning workloads on AWS (March 2019)
Optimize your machine learning workloads on AWS (March 2019)
Julien SIMON
Build, Train and Deploy Machine Learning Models at Scale (April 2019)
Build, Train and Deploy Machine Learning Models at Scale (April 2019)
Julien SIMON
A pragmatic introduction to natural language processing models (October 2019)
A pragmatic introduction to natural language processing models (October 2019)
Julien SIMON
Building Machine Learning Inference Pipelines at Scale (July 2019)
Building Machine Learning Inference Pipelines at Scale (July 2019)
Julien SIMON
Machine Learning on AWS (December 2018)
Machine Learning on AWS (December 2018)
Julien SIMON
Scaling Machine Learning from zero to millions of users (May 2019)
Scaling Machine Learning from zero to millions of users (May 2019)
Julien SIMON
Get started with Machine Learning and Computer Vision Using AWS DeepLens (Feb...
Get started with Machine Learning and Computer Vision Using AWS DeepLens (Feb...
Julien SIMON
Build Machine Learning Models with Amazon SageMaker (April 2019)
Build Machine Learning Models with Amazon SageMaker (April 2019)
Julien SIMON
Starting your AI/ML project right (May 2020)
Starting your AI/ML project right (May 2020)
Julien SIMON
Amazon SageMaker (December 2018)
Amazon SageMaker (December 2018)
Julien SIMON
Building smart applications with AWS AI services (October 2019)
Building smart applications with AWS AI services (October 2019)
Julien SIMON
Automate your Amazon SageMaker Workflows (July 2019)
Automate your Amazon SageMaker Workflows (July 2019)
Julien SIMON
The Future of AI (September 2019)
The Future of AI (September 2019)
Julien SIMON
Building machine learning inference pipelines at scale (March 2019)
Building machine learning inference pipelines at scale (March 2019)
Julien SIMON
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
Julien SIMON
Deep Learning on Amazon Sagemaker (July 2019)
Deep Learning on Amazon Sagemaker (July 2019)
Julien SIMON
Machine Learning & Amazon SageMaker
Machine Learning & Amazon SageMaker
Amazon Web Services
AWS re:Invent 2018 - AIM302 - Machine Learning at the Edge
AWS re:Invent 2018 - AIM302 - Machine Learning at the Edge
Julien SIMON
Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)
Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)
Julien SIMON
Optimize your Machine Learning workloads (April 2019)
Optimize your Machine Learning workloads (April 2019)
Julien SIMON
Working with Amazon SageMaker Algorithms for Faster Model Training
Working with Amazon SageMaker Algorithms for Faster Model Training
Amazon Web Services
AWS re:Invent 2018 - AIM401 - Deep Learning using Tensorflow
AWS re:Invent 2018 - AIM401 - Deep Learning using Tensorflow
Julien SIMON
Amazon SageMaker Deep Dive - Meetup AWS Toulouse at D2SI
Amazon SageMaker Deep Dive - Meetup AWS Toulouse at D2SI
Amazon Web Services
Introduction to Sagemaker
Introduction to Sagemaker
Amazon Web Services
Aws autopilot
Aws autopilot
Vivek Raja P S
Become a Machine Learning Developer with AWS Services
Become a Machine Learning Developer with AWS Services
Amazon Web Services
Become a Machine Learning developer with AWS (Avril 2019)
Become a Machine Learning developer with AWS (Avril 2019)
Julien SIMON
Contenu connexe
Tendances
Scaling Machine Learning from zero to millions of users (May 2019)
Scaling Machine Learning from zero to millions of users (May 2019)
Julien SIMON
Get started with Machine Learning and Computer Vision Using AWS DeepLens (Feb...
Get started with Machine Learning and Computer Vision Using AWS DeepLens (Feb...
Julien SIMON
Build Machine Learning Models with Amazon SageMaker (April 2019)
Build Machine Learning Models with Amazon SageMaker (April 2019)
Julien SIMON
Starting your AI/ML project right (May 2020)
Starting your AI/ML project right (May 2020)
Julien SIMON
Amazon SageMaker (December 2018)
Amazon SageMaker (December 2018)
Julien SIMON
Building smart applications with AWS AI services (October 2019)
Building smart applications with AWS AI services (October 2019)
Julien SIMON
Automate your Amazon SageMaker Workflows (July 2019)
Automate your Amazon SageMaker Workflows (July 2019)
Julien SIMON
The Future of AI (September 2019)
The Future of AI (September 2019)
Julien SIMON
Building machine learning inference pipelines at scale (March 2019)
Building machine learning inference pipelines at scale (March 2019)
Julien SIMON
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
Julien SIMON
Deep Learning on Amazon Sagemaker (July 2019)
Deep Learning on Amazon Sagemaker (July 2019)
Julien SIMON
Machine Learning & Amazon SageMaker
Machine Learning & Amazon SageMaker
Amazon Web Services
AWS re:Invent 2018 - AIM302 - Machine Learning at the Edge
AWS re:Invent 2018 - AIM302 - Machine Learning at the Edge
Julien SIMON
Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)
Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)
Julien SIMON
Optimize your Machine Learning workloads (April 2019)
Optimize your Machine Learning workloads (April 2019)
Julien SIMON
Working with Amazon SageMaker Algorithms for Faster Model Training
Working with Amazon SageMaker Algorithms for Faster Model Training
Amazon Web Services
AWS re:Invent 2018 - AIM401 - Deep Learning using Tensorflow
AWS re:Invent 2018 - AIM401 - Deep Learning using Tensorflow
Julien SIMON
Amazon SageMaker Deep Dive - Meetup AWS Toulouse at D2SI
Amazon SageMaker Deep Dive - Meetup AWS Toulouse at D2SI
Amazon Web Services
Introduction to Sagemaker
Introduction to Sagemaker
Amazon Web Services
Aws autopilot
Aws autopilot
Vivek Raja P S
Tendances
(20)
Scaling Machine Learning from zero to millions of users (May 2019)
Scaling Machine Learning from zero to millions of users (May 2019)
Get started with Machine Learning and Computer Vision Using AWS DeepLens (Feb...
Get started with Machine Learning and Computer Vision Using AWS DeepLens (Feb...
Build Machine Learning Models with Amazon SageMaker (April 2019)
Build Machine Learning Models with Amazon SageMaker (April 2019)
Starting your AI/ML project right (May 2020)
Starting your AI/ML project right (May 2020)
Amazon SageMaker (December 2018)
Amazon SageMaker (December 2018)
Building smart applications with AWS AI services (October 2019)
Building smart applications with AWS AI services (October 2019)
Automate your Amazon SageMaker Workflows (July 2019)
Automate your Amazon SageMaker Workflows (July 2019)
The Future of AI (September 2019)
The Future of AI (September 2019)
Building machine learning inference pipelines at scale (March 2019)
Building machine learning inference pipelines at scale (March 2019)
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
Deep Learning on Amazon Sagemaker (July 2019)
Deep Learning on Amazon Sagemaker (July 2019)
Machine Learning & Amazon SageMaker
Machine Learning & Amazon SageMaker
AWS re:Invent 2018 - AIM302 - Machine Learning at the Edge
AWS re:Invent 2018 - AIM302 - Machine Learning at the Edge
Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)
Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)
Optimize your Machine Learning workloads (April 2019)
Optimize your Machine Learning workloads (April 2019)
Working with Amazon SageMaker Algorithms for Faster Model Training
Working with Amazon SageMaker Algorithms for Faster Model Training
AWS re:Invent 2018 - AIM401 - Deep Learning using Tensorflow
AWS re:Invent 2018 - AIM401 - Deep Learning using Tensorflow
Amazon SageMaker Deep Dive - Meetup AWS Toulouse at D2SI
Amazon SageMaker Deep Dive - Meetup AWS Toulouse at D2SI
Introduction to Sagemaker
Introduction to Sagemaker
Aws autopilot
Aws autopilot
Similaire à Become a Machine Learning developer with AWS services (May 2019)
Become a Machine Learning Developer with AWS Services
Become a Machine Learning Developer with AWS Services
Amazon Web Services
Become a Machine Learning developer with AWS (Avril 2019)
Become a Machine Learning developer with AWS (Avril 2019)
Julien SIMON
Build-Train-Deploy-Machine-Learning-Models-at-Any-Scale
Build-Train-Deploy-Machine-Learning-Models-at-Any-Scale
Amazon Web Services
[AWS Techshift] Innovation and AI/ML Sagemaker Build-in 머신러닝 모델 활용 및 Marketpl...
[AWS Techshift] Innovation and AI/ML Sagemaker Build-in 머신러닝 모델 활용 및 Marketpl...
Amazon Web Services Korea
Fraud Prevention and Detection on AWS
Fraud Prevention and Detection on AWS
Amazon Web Services
WhereML a Serverless ML Powered Location Guessing Twitter Bot
WhereML a Serverless ML Powered Location Guessing Twitter Bot
Randall Hunt
Machine Learning: From Inception to Inference - AWS Summit Sydney
Machine Learning: From Inception to Inference - AWS Summit Sydney
Amazon Web Services
Building intelligent applications using AI services
Building intelligent applications using AI services
Amazon Web Services
AWS及客戶在AI/ML的數位運行過程中得到的重要經驗與學習
AWS及客戶在AI/ML的數位運行過程中得到的重要經驗與學習
Amazon Web Services
Artificial intelligence in actions: delivering a new experience to Formula 1 ...
Artificial intelligence in actions: delivering a new experience to Formula 1 ...
GoDataDriven
Amazon SageMaker Build, Train and Deploy Your ML Models
Amazon SageMaker Build, Train and Deploy Your ML Models
AWS Riyadh User Group
Automate Security Event Management Using Trust-Based Decision Models - AWS Su...
Automate Security Event Management Using Trust-Based Decision Models - AWS Su...
Amazon Web Services
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Amazon Web Services
Best of re:Invent for Startups
Best of re:Invent for Startups
Amazon Web Services
Rendi le tue app più smart con i servizi AI di AWS
Rendi le tue app più smart con i servizi AI di AWS
Amazon Web Services
Machine Learning and IoT on AWS
Machine Learning and IoT on AWS
Amazon Web Services
Build, train and deploy machine learning models at scale using AWS
Build, train and deploy machine learning models at scale using AWS
Amazon Web Services
The Next Wave of Retailing, An AWS Perspective - Tom Litchford 월드와이드 리테일 사업 개...
The Next Wave of Retailing, An AWS Perspective - Tom Litchford 월드와이드 리테일 사업 개...
Amazon Web Services Korea
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Amazon Web Services
Fraud detection using machine learning with Amazon SageMaker - AIM306 - New Y...
Fraud detection using machine learning with Amazon SageMaker - AIM306 - New Y...
Amazon Web Services
Similaire à Become a Machine Learning developer with AWS services (May 2019)
(20)
Become a Machine Learning Developer with AWS Services
Become a Machine Learning Developer with AWS Services
Become a Machine Learning developer with AWS (Avril 2019)
Become a Machine Learning developer with AWS (Avril 2019)
Build-Train-Deploy-Machine-Learning-Models-at-Any-Scale
Build-Train-Deploy-Machine-Learning-Models-at-Any-Scale
[AWS Techshift] Innovation and AI/ML Sagemaker Build-in 머신러닝 모델 활용 및 Marketpl...
[AWS Techshift] Innovation and AI/ML Sagemaker Build-in 머신러닝 모델 활용 및 Marketpl...
Fraud Prevention and Detection on AWS
Fraud Prevention and Detection on AWS
WhereML a Serverless ML Powered Location Guessing Twitter Bot
WhereML a Serverless ML Powered Location Guessing Twitter Bot
Machine Learning: From Inception to Inference - AWS Summit Sydney
Machine Learning: From Inception to Inference - AWS Summit Sydney
Building intelligent applications using AI services
Building intelligent applications using AI services
AWS及客戶在AI/ML的數位運行過程中得到的重要經驗與學習
AWS及客戶在AI/ML的數位運行過程中得到的重要經驗與學習
Artificial intelligence in actions: delivering a new experience to Formula 1 ...
Artificial intelligence in actions: delivering a new experience to Formula 1 ...
Amazon SageMaker Build, Train and Deploy Your ML Models
Amazon SageMaker Build, Train and Deploy Your ML Models
Automate Security Event Management Using Trust-Based Decision Models - AWS Su...
Automate Security Event Management Using Trust-Based Decision Models - AWS Su...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Best of re:Invent for Startups
Best of re:Invent for Startups
Rendi le tue app più smart con i servizi AI di AWS
Rendi le tue app più smart con i servizi AI di AWS
Machine Learning and IoT on AWS
Machine Learning and IoT on AWS
Build, train and deploy machine learning models at scale using AWS
Build, train and deploy machine learning models at scale using AWS
The Next Wave of Retailing, An AWS Perspective - Tom Litchford 월드와이드 리테일 사업 개...
The Next Wave of Retailing, An AWS Perspective - Tom Litchford 월드와이드 리테일 사업 개...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Fraud detection using machine learning with Amazon SageMaker - AIM306 - New Y...
Fraud detection using machine learning with Amazon SageMaker - AIM306 - New Y...
Plus de Julien SIMON
An introduction to computer vision with Hugging Face
An introduction to computer vision with Hugging Face
Julien SIMON
Reinventing Deep Learning with Hugging Face Transformers
Reinventing Deep Learning with Hugging Face Transformers
Julien SIMON
Building NLP applications with Transformers
Building NLP applications with Transformers
Julien SIMON
Building Machine Learning Models Automatically (June 2020)
Building Machine Learning Models Automatically (June 2020)
Julien SIMON
Scale Machine Learning from zero to millions of users (April 2020)
Scale Machine Learning from zero to millions of users (April 2020)
Julien SIMON
An Introduction to Generative Adversarial Networks (April 2020)
An Introduction to Generative Adversarial Networks (April 2020)
Julien SIMON
AIM410R1 Deep learning applications with TensorFlow, featuring Fannie Mae (De...
AIM410R1 Deep learning applications with TensorFlow, featuring Fannie Mae (De...
Julien SIMON
AIM361 Optimizing machine learning models with Amazon SageMaker (December 2019)
AIM361 Optimizing machine learning models with Amazon SageMaker (December 2019)
Julien SIMON
Train and Deploy Machine Learning Workloads with AWS Container Services (July...
Train and Deploy Machine Learning Workloads with AWS Container Services (July...
Julien SIMON
Optimize your Machine Learning Workloads on AWS (July 2019)
Optimize your Machine Learning Workloads on AWS (July 2019)
Julien SIMON
Solve complex business problems with Amazon Personalize and Amazon Forecast (...
Solve complex business problems with Amazon Personalize and Amazon Forecast (...
Julien SIMON
Deep Learning with Tensorflow and Apache MXNet on AWS (April 2019)
Deep Learning with Tensorflow and Apache MXNet on AWS (April 2019)
Julien SIMON
Plus de Julien SIMON
(12)
An introduction to computer vision with Hugging Face
An introduction to computer vision with Hugging Face
Reinventing Deep Learning with Hugging Face Transformers
Reinventing Deep Learning with Hugging Face Transformers
Building NLP applications with Transformers
Building NLP applications with Transformers
Building Machine Learning Models Automatically (June 2020)
Building Machine Learning Models Automatically (June 2020)
Scale Machine Learning from zero to millions of users (April 2020)
Scale Machine Learning from zero to millions of users (April 2020)
An Introduction to Generative Adversarial Networks (April 2020)
An Introduction to Generative Adversarial Networks (April 2020)
AIM410R1 Deep learning applications with TensorFlow, featuring Fannie Mae (De...
AIM410R1 Deep learning applications with TensorFlow, featuring Fannie Mae (De...
AIM361 Optimizing machine learning models with Amazon SageMaker (December 2019)
AIM361 Optimizing machine learning models with Amazon SageMaker (December 2019)
Train and Deploy Machine Learning Workloads with AWS Container Services (July...
Train and Deploy Machine Learning Workloads with AWS Container Services (July...
Optimize your Machine Learning Workloads on AWS (July 2019)
Optimize your Machine Learning Workloads on AWS (July 2019)
Solve complex business problems with Amazon Personalize and Amazon Forecast (...
Solve complex business problems with Amazon Personalize and Amazon Forecast (...
Deep Learning with Tensorflow and Apache MXNet on AWS (April 2019)
Deep Learning with Tensorflow and Apache MXNet on AWS (April 2019)
Dernier
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
Khushali Kathiriya
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
Anna Loughnan Colquhoun
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
apidays
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Radu Cotescu
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
UK Journal
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
Product Anonymous
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Drew Madelung
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Roshan Dwivedi
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
DianaGray10
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Safe Software
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
jfdjdjcjdnsjd
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
apidays
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Edi Saputra
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
Andrey Devyatkin
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Gabriella Davis
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
wesley chun
Dernier
(20)
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
Become a Machine Learning developer with AWS services (May 2019)
1.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Become a Machine Learning developer with AWS services Julien Simon Global Evangelist, AI & Machine Learning, AWS @julsimon Lars Hoogweg CTO, Lebara
2.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Machine learning cycle Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training
3.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build your dataset Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training
4.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Annotating data at scale is time-consuming and expensive
5.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker Ground Truth Buildscalableandcost-effectivelabelingworkflows Easily integrate human labelers Get accurate results K E Y F E AT U R E S Automatic labeling via machine learning Ready-made and custom workflows for image bounding box, segmentation, and text Quickly label training data Private and public human workforce
6.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Prepare your dataset for Machine Learning Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training
7.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build, train and deploy models using compute services Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training Amazon EC2 Amazon EKS Amazon ECS AWS Batch
8.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. AWS Deep Learning AMIs Preconfigured environments on Amazon Linux or Ubuntu Conda AMI For developers who want pre- installed pip packages of DL frameworks in separate virtual environments. Base AMI For developers who want a clean slate to set up private DL engine repositories or custom builds of DL engines. AMI with source code For developers who want preinstalled DL frameworks and their source code in a shared Python environment.
9.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build, train and deploy models using SageMaker Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training
10.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Model options Training code Factorization Machines Linear Learner Principal Component Analysis K-Means Clustering XGBoost And more Built-in Algorithms (17) No ML coding required No infrastructure work required Distributed training Pipe mode Bring Your Own Container Full control, run anything! R, C++, etc. No infrastructure work required Built-in Frameworks Bring your own code: script mode Open source containers No infrastructure work required Distributed training Pipe mode
11.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Using Machine Learning to detect Telco Fraud Lars Hoogweg Chief Technology Officer Lebara
12.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Agenda About Lebara Telco Fraud Using ML for detecting Telco Fraud Next Steps
13.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T About Lebara • Mobile Virtual Network Operator • Active in 5 countries across Europe • Our mission: to make it easier for migrant communities to stay connected to family and friends back home
14.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T What is Telco Fraud? “the use of telecommunications products or services with the intention of illegally acquiring money from a telecommunication company or its customers”
15.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Telco Fraud Examples • SIM Boxing • A SIM box is a device containing a number of SIM cards. These SIM cards are used to terminate (international) calls bypassing international interconnect charges • One A-number calling many different B-numbers • Revenue Share Fraud • Generate traffic to high cost, revenue share service numbers • Multiple A-numbers calling the same B-number or range of B-numbers. • Higher than average call duration • Wangiri Fraud • A special case of Revenue Share Fraud • Making random calls from premium rate numbers, letting the calls ring once and then hanging up, hoping that recipients call back
16.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Fraud Detection @ Lebara • Current fraud detection approach is rule based • Fraudsters may change their patterns when they hit these rules • We cannot detect the fraud we do not know • Can we use ML to improve our fraud detection capabilities? • Automating fraud detection • Detecting new types of fraud? • How do we find out given our limited knowledge of ML?
17.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Approach • Organized a three-day offsite workshop together with AWS ML experts • Working with actual Lebara data: Call Detail Records (CDRs) • Data set labeled using existing fraud system • Three groups focusing on three different types of fraud • Focus on Revenue Share Fraud for the rest of this presentation • Training and deploying models with Amazon SageMaker
18.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Call Detail Records (CDR) • For each call, SMS, data session, top up, etc., a CDR is generated in real-time by Lebara’s Online Charging System • Lebara streams CDRs using Amazon Kinesis Firehose and stores them in Amazon S3 • So, what does a CDR look like?
19.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T An Example Call Detail Record 704000001796849823|0|20190504205402|1|31616531654|31624586868|31616531654||0624586868||1| 00|316530200000|204083309537469|||20190504215245|8|0|0|20190504215254|0|68|1|304543413330 43443337||120||||1287862183|310008|31616531654|1|0|20190501|0|0|0|0||31|99|1|31|99|99999|3 1|99|2|31|99|2|310008|1000000000000000000|0|0|0||||1000000|102779300616118593|2||0|120|0|0 |192440000|0|0|102779200616117893|0||6372|0|120|179880|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0| 0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|||||||0|0|0||||||||20190501000000||||0|0|0|0|0|0|0||0|0| 0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0||||31624586868||||ocg2;1556999564;69969258;2|||||||||||1| N|N|201905041950|20190504215402|D|11000|11|102779300616118593|102779200616117893|5010000 0060964541NLD|50100000060964541NLD|1003|1|0|0|0|0|0|0|0|0|0|50100000060964541NLD|1027793 00616118593|50100000060964541NLD|0|0|0|||0|0|||0|0||||0|||0|0|||0|0||||0|||0|0|||0|0||||0||| 0|0|||0|0||||0||F|S|31616531654|704000000967825550|1003|0|179880|319903||||0|0|0|0|0|0|0||| ||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0 |0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0| 0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0||0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0 |0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0| |0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|||||||316530200000|0|0|0|1|73253560
20.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T An Example Call Detail Record 704000001796849823|0|20190504205402|1|31616531654|31624586868|31616531654||0624586868||1| 00|316530200000|204083309537469|||20190504215245|8|0|0|20190504215254|0|68|1|304543413330 43443337||120||||1287862183|310008|31616531654|1|0|20190501|0|0|0|0||31|99|1|31|99|99999|3 1|99|2|31|99|2|310008|1000000000000000000|0|0|0||||1000000|102779300616118593|2||0|120|0|0 |192440000|0|0|102779200616117893|0||6372|0|120|179880|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0| 0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|||||||0|0|0||||||||20190501000000||||0|0|0|0|0|0|0||0|0| 0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0||||31624586868||||ocg2;1556999564;69969258;2|||||||||||1| N|N|201905041950|20190504215402|D|11000|11|102779300616118593|102779200616117893|5010000 0060964541NLD|50100000060964541NLD|1003|1|0|0|0|0|0|0|0|0|0|50100000060964541NLD|1027793 00616118593|50100000060964541NLD|0|0|0|||0|0|||0|0||||0|||0|0|||0|0||||0|||0|0|||0|0||||0||| 0|0|||0|0||||0||F|S|31616531654|704000000967825550|1003|0|179880|319903||||0|0|0|0|0|0|0||| ||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0 |0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0| 0|0|0|0|0|||||0|0|0|0|0|0|0|||||0|0|0|0|0|0|0||0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0 |0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0| |0|0|0||0|0|0|0|0||0|0|0||0|0|0|0|0||0|||||||316530200000|0|0|0|1|73253560 Timestamp2019-05-04 21:52:54 A-number 31616531654 B-number 31624586868 Duration
21.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Data preparation • A significant amount of time was spent analyzing and preparing the data • Removing calls to non-numeric or too long B-numbers • Filtering out calls to short numbers, like IVR and CS as these are certainly not fraud and may skew the results (many A-numbers calling a few B-numbers)
22.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Feature Engineering • Creating the variables used to train the machine learning model • Features that could be used for detecting Revenue Share Fraud • Time of day / day of week • Count of different A-numbers calling a B-number range within a given time window • Ratio of A- to B-numbers • Average call duration / standard deviation
23.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Using built-in algorithms in Amazon SageMaker • Unsupervised learning for anomaly detection • Algorithm used: Random Cut Forest • Actual (previously unknown) fraud detected! • Supervised learning using our labeled dataset • A needle in a hay-stack: only 1 in every 3000 calls is considered fraudulent • Algorithm used: XGBoost • Despite the extreme unbalance, initial results are promising • Next step is tuning the model to reduce the number of false negatives Confusion Matrix Prediction Not Fraud Fraud Actual Not Fraud 114010 0 Fraud 417 187
24.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Conclusions • Using Amazon SageMaker, Lebara could get started with limited prior ML knowledge • Lebara managed to achieve promising results for detecting telco fraud within days • Besides continuing work on the fraud detection use case, we are looking at applying ML in other areas as well
25.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Hands-on with Amazon SageMaker
26.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T The Amazon SageMaker API • Python SDK orchestrating all Amazon SageMaker activity • High-level objects for algorithm selection, training, deploying, model tuning, etc. • Spark SDK too (Python & Scala) • AWS SDK • For scripting and automation • CLI : ‘aws sagemaker’ • Language SDKs: boto3, etc.
27.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Demo: Automatic Model Tuning with XGBoost https://gitlab.com/juliensimon/ent321/blob/master/ENT321%20- %20short%20version.ipynb
28.
© 2019, Amazon
Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Getting started http://aws.amazon.com/free https://aws.amazon.com/sagemaker https://github.com/aws/sagemaker-python-sdk https://github.com/aws/sagemaker-spark https://github.com/awslabs/amazon-sagemaker-examples https://gitlab.com/juliensimon/ent321 https://medium.com/@julsimon https://gitlab.com/juliensimon/dlnotebooks https://gitlab.com/juliensimon/dlcontainers
29.
Thank you! S U
M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning, AWS @julsimon Lars Hoogweg CTO, Lebara
30.
S U M
M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Télécharger maintenant