SlideShare una empresa de Scribd logo

Deep Learning at the Edge with AWS

•
•
Julien SIMON
Julien SIMON

Talk @ Papis.io, London, 05/04/2018

Tecnología
1 de 14
Descargar para leer sin conexión
© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Julien Simon, Principal Evangelist, AI & Machine Learning @julsimon Deep Learning at the Edge
Deep Learning at the Edge 1. Flexible experimentation in the Cloud. 2. Scalable training in the Cloud. 3. Good prediction performance at the Edge. 4. Simple deployment of code and model
Flexible experimentation in the Cloud • Apache MXNet: Python, R, Perl, Matlab, Scala, C++. • Gluon • Imperative programming aka ‘define-by-run’. • Inspect, debug and modify models during training. • Extensive model zoo • Pre-trained computer vision models. • MobileNet, SqueezeNet for resource-constrained devices.
Scalable training in the Cloud Amazon SageMaker AWS Deep Learning AMI Amazon EC2 c5 p3
Good prediction performance at the Edge • MXNet is written in C++. • Gluon networks can be ‘hybridized’ for additional speed. • Two libraries boost performance on CPU-only devices • Fast implementation of math primitives • Hardware-specific instructions, e.g. Intel AVX or ARM NEON • Intel Math Kernel Library https://software.intel.com/en-us/mkl • NNPACK https://github.com/Maratyszcza/NNPACK • Mixed precision training • Use float16 instead of float32 for weights and activations • Almost 2x reduction in model size, no loss of accuracy, faster inference • https://devblogs.nvidia.com/parallelforall/mixed-precision-training-deep-neural-networks/
Simple deployment of code and model • Train a model in SageMaker (or bring your own). • Write a Lambda function performing prediction. • Add both as resources in your Greengrass group. • Let Greengrass handle deployment and updates. Best when You want the same programming model in the Cloud and at the Edge. Code and models need to be updated, even if network connectivity is infrequent or unreliable. One device in the group should be able to perform prediction on behalf on other devices. Requirements Devices are powerful enough to run Greengrass (XXX HW requirements) Devices are provisioned in AWS IoT (certificate, keys).
ML Inference using AWS Greengrass Deploy model and Lambda separately Send insights Predict and take actions locally on device AWS Cloud
AWS DeepLens Intel Atom CPU Gen9 graphics Ubuntu 16.04 LTS 100 GFLOPS performance Dual band Wi-Fi 8 GB RAM 16 GB Storage (eMMC) 32 GB SD card 4 MP camera with MJPEG H.264 encoding at 1080p resolution 2 USB ports Micro HDMI Audio out AWS Greengrass preconfigured Intel clDNN for Apache MXNet
AWS DeepLens Architecture Video out Data out D E P L O Y P R O J E C T S Manage device Console Project Management I N F E R E N C E AWS Cloud Intel Model Optimizer
AWS DeepLens
Object detection with AWS DeepLens
Resources
Resources https://mxnet.incubator.apache.org http://gluon.mxnet.io https://aws.amazon.com/sagemaker (free tier available) An overview of Amazon SageMaker: https://www.youtube.com/watch?v=ym7NEYEx9x4 https://github.com/awslabs/amazon-sagemaker-examples https://aws.amazon.com/greengrass (free tier available) https://aws.amazon.com/deeplens https://github.com/intel/clDNN https://medium.com/@julsimon You’ll pay for this.
© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Thank you! Julien Simon, Principal Evangelist, AI & Machine Learning @julsimon

Recomendados

Machine Learning Inference at the Edge
Machine Learning Inference at the EdgeMachine Learning Inference at the Edge
Machine Learning Inference at the EdgeJulien SIMON
 
Machine Learning inference at the Edge
Machine Learning inference at the EdgeMachine Learning inference at the Edge
Machine Learning inference at the EdgeJulien SIMON
 
ML Inference at the Edge
ML Inference at the EdgeML Inference at the Edge
ML Inference at the EdgeAmazon Web Services
 
Amazon SageMaker workshop
Amazon SageMaker workshopAmazon SageMaker workshop
Amazon SageMaker workshopJulien SIMON
 
Deep Dive on Deep Learning (June 2018)
Deep Dive on Deep Learning (June 2018)Deep Dive on Deep Learning (June 2018)
Deep Dive on Deep Learning (June 2018)Julien SIMON
 
Build, train, and deploy Machine Learning models at scale (May 2018)
Build, train, and deploy Machine Learning models at scale (May 2018)Build, train, and deploy Machine Learning models at scale (May 2018)
Build, train, and deploy Machine Learning models at scale (May 2018)Julien SIMON
 
An Introduction to Deep Learning (May 2018)
An Introduction to Deep Learning (May 2018)An Introduction to Deep Learning (May 2018)
An Introduction to Deep Learning (May 2018)Julien SIMON
 
Speed up your Machine Learning workflows with build-in algorithms
Speed up your Machine Learning workflows with build-in algorithmsSpeed up your Machine Learning workflows with build-in algorithms
Speed up your Machine Learning workflows with build-in algorithmsJulien SIMON
 

Recomendados

Machine Learning Inference at the Edge
Machine Learning Inference at the EdgeMachine Learning Inference at the Edge
Machine Learning Inference at the EdgeJulien SIMON
 
Machine Learning inference at the Edge
Machine Learning inference at the EdgeMachine Learning inference at the Edge
Machine Learning inference at the EdgeJulien SIMON
 
ML Inference at the Edge
ML Inference at the EdgeML Inference at the Edge
ML Inference at the EdgeAmazon Web Services
 
Amazon SageMaker workshop
Amazon SageMaker workshopAmazon SageMaker workshop
Amazon SageMaker workshopJulien SIMON
 
Deep Dive on Deep Learning (June 2018)
Deep Dive on Deep Learning (June 2018)Deep Dive on Deep Learning (June 2018)
Deep Dive on Deep Learning (June 2018)Julien SIMON
 
Build, train, and deploy Machine Learning models at scale (May 2018)
Build, train, and deploy Machine Learning models at scale (May 2018)Build, train, and deploy Machine Learning models at scale (May 2018)
Build, train, and deploy Machine Learning models at scale (May 2018)Julien SIMON
 
An Introduction to Deep Learning (May 2018)
An Introduction to Deep Learning (May 2018)An Introduction to Deep Learning (May 2018)
An Introduction to Deep Learning (May 2018)Julien SIMON
 
Speed up your Machine Learning workflows with build-in algorithms
Speed up your Machine Learning workflows with build-in algorithmsSpeed up your Machine Learning workflows with build-in algorithms
Speed up your Machine Learning workflows with build-in algorithmsJulien SIMON
 
Machine Learning Models with Apache MXNet and AWS Fargate
Machine Learning Models with Apache MXNet and AWS FargateMachine Learning Models with Apache MXNet and AWS Fargate
Machine Learning Models with Apache MXNet and AWS FargateAmazon Web Services
 
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...Amazon Web Services
 
ECS for Amazon Deep Learning and Amazon Machine Learning
ECS for Amazon Deep Learning and Amazon Machine LearningECS for Amazon Deep Learning and Amazon Machine Learning
ECS for Amazon Deep Learning and Amazon Machine LearningAmanda Mackay (she/her)
 
Machine Learning in Action
Machine Learning in ActionMachine Learning in Action
Machine Learning in ActionAmazon Web Services
 
FPGAs in the cloud? (October 2017)
FPGAs in the cloud? (October 2017)FPGAs in the cloud? (October 2017)
FPGAs in the cloud? (October 2017)Julien SIMON
 
Scalable Deep Learning on AWS with Apache MXNet
Scalable Deep Learning on AWS with Apache MXNetScalable Deep Learning on AWS with Apache MXNet
Scalable Deep Learning on AWS with Apache MXNetJulien SIMON
 
Deep Learning with Apache MXNet (September 2017)
Deep Learning with Apache MXNet (September 2017)Deep Learning with Apache MXNet (September 2017)
Deep Learning with Apache MXNet (September 2017)Julien SIMON
 
Build, Train & Deploy Machine Learning Models at Scale
Build, Train & Deploy Machine Learning Models at ScaleBuild, Train & Deploy Machine Learning Models at Scale
Build, Train & Deploy Machine Learning Models at ScaleAmazon Web Services
 
Build, Train, and Deploy ML Models at Scale
Build, Train, and Deploy ML Models at ScaleBuild, Train, and Deploy ML Models at Scale
Build, Train, and Deploy ML Models at ScaleAmazon Web Services
 
Optimize your Machine Learning Workloads on AWS (July 2019)
Optimize your Machine Learning Workloads on AWS (July 2019)Optimize your Machine Learning Workloads on AWS (July 2019)
Optimize your Machine Learning Workloads on AWS (July 2019)Julien SIMON
 
Build, train, and deploy ML models at scale.pdf
Build, train, and deploy ML models at scale.pdfBuild, train, and deploy ML models at scale.pdf
Build, train, and deploy ML models at scale.pdfAmazon Web Services
 
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Amazon Web Services
 
Intel and Amazon - Powering your innovation together.
Intel and Amazon - Powering your innovation together. Intel and Amazon - Powering your innovation together.
Intel and Amazon - Powering your innovation together. Eran Shlomo
 
Using Amazon SageMaker to build, train, and deploy your ML Models
Using Amazon SageMaker to build, train, and deploy your ML ModelsUsing Amazon SageMaker to build, train, and deploy your ML Models
Using Amazon SageMaker to build, train, and deploy your ML ModelsAmazon Web Services
 
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMaker
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMakerAWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMaker
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMakerAmazon Web Services
 
Scalable Deep Learning on AWS using Apache MXNet (May 2017)
Scalable Deep Learning on AWS using Apache MXNet (May 2017)Scalable Deep Learning on AWS using Apache MXNet (May 2017)
Scalable Deep Learning on AWS using Apache MXNet (May 2017)Julien SIMON
 
What Can HPC on AWS Do?
What Can HPC on AWS Do?What Can HPC on AWS Do?
What Can HPC on AWS Do?inside-BigData.com
 
Using Amazon SageMaker to build, train, & deploy your ML Models
Using Amazon SageMaker to build, train, & deploy your ML ModelsUsing Amazon SageMaker to build, train, & deploy your ML Models
Using Amazon SageMaker to build, train, & deploy your ML ModelsAmazon Web Services
 
Accelerate your Machine Learning workflows with Amazon SageMaker
Accelerate your Machine Learning workflows with Amazon SageMakerAccelerate your Machine Learning workflows with Amazon SageMaker
Accelerate your Machine Learning workflows with Amazon SageMakerJulien SIMON
 
Picking the right AWS backend for your Java application
Picking the right AWS backend for your Java applicationPicking the right AWS backend for your Java application
Picking the right AWS backend for your Java applicationJulien SIMON
 
Machine Learning Inference at the Edge
Machine Learning Inference at the EdgeMachine Learning Inference at the Edge
Machine Learning Inference at the EdgeAmazon Web Services
 
DataPalooza - A Music Festival themed ML + IoT Workshop
DataPalooza - A Music Festival themed ML + IoT WorkshopDataPalooza - A Music Festival themed ML + IoT Workshop
DataPalooza - A Music Festival themed ML + IoT WorkshopAmazon Web Services
 

Más contenido relacionado

La actualidad más candente

Machine Learning Models with Apache MXNet and AWS Fargate
Machine Learning Models with Apache MXNet and AWS FargateMachine Learning Models with Apache MXNet and AWS Fargate
Machine Learning Models with Apache MXNet and AWS FargateAmazon Web Services
 
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...Amazon Web Services
 
ECS for Amazon Deep Learning and Amazon Machine Learning
ECS for Amazon Deep Learning and Amazon Machine LearningECS for Amazon Deep Learning and Amazon Machine Learning
ECS for Amazon Deep Learning and Amazon Machine LearningAmanda Mackay (she/her)
 
FPGAs in the cloud? (October 2017)
FPGAs in the cloud? (October 2017)FPGAs in the cloud? (October 2017)
FPGAs in the cloud? (October 2017)Julien SIMON
 
Scalable Deep Learning on AWS with Apache MXNet
Scalable Deep Learning on AWS with Apache MXNetScalable Deep Learning on AWS with Apache MXNet
Scalable Deep Learning on AWS with Apache MXNetJulien SIMON
 
Deep Learning with Apache MXNet (September 2017)
Deep Learning with Apache MXNet (September 2017)Deep Learning with Apache MXNet (September 2017)
Deep Learning with Apache MXNet (September 2017)Julien SIMON
 
Build, Train & Deploy Machine Learning Models at Scale
Build, Train & Deploy Machine Learning Models at ScaleBuild, Train & Deploy Machine Learning Models at Scale
Build, Train & Deploy Machine Learning Models at ScaleAmazon Web Services
 
Build, Train, and Deploy ML Models at Scale
Build, Train, and Deploy ML Models at ScaleBuild, Train, and Deploy ML Models at Scale
Build, Train, and Deploy ML Models at ScaleAmazon Web Services
 
Optimize your Machine Learning Workloads on AWS (July 2019)
Optimize your Machine Learning Workloads on AWS (July 2019)Optimize your Machine Learning Workloads on AWS (July 2019)
Optimize your Machine Learning Workloads on AWS (July 2019)Julien SIMON
 
Build, train, and deploy ML models at scale.pdf
Build, train, and deploy ML models at scale.pdfBuild, train, and deploy ML models at scale.pdf
Build, train, and deploy ML models at scale.pdfAmazon Web Services
 
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Amazon Web Services
 
Intel and Amazon - Powering your innovation together.
Intel and Amazon - Powering your innovation together. Intel and Amazon - Powering your innovation together.
Intel and Amazon - Powering your innovation together. Eran Shlomo
 
Using Amazon SageMaker to build, train, and deploy your ML Models
Using Amazon SageMaker to build, train, and deploy your ML ModelsUsing Amazon SageMaker to build, train, and deploy your ML Models
Using Amazon SageMaker to build, train, and deploy your ML ModelsAmazon Web Services
 
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMaker
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMakerAWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMaker
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMakerAmazon Web Services
 
Scalable Deep Learning on AWS using Apache MXNet (May 2017)
Scalable Deep Learning on AWS using Apache MXNet (May 2017)Scalable Deep Learning on AWS using Apache MXNet (May 2017)
Scalable Deep Learning on AWS using Apache MXNet (May 2017)Julien SIMON
 
Using Amazon SageMaker to build, train, & deploy your ML Models
Using Amazon SageMaker to build, train, & deploy your ML ModelsUsing Amazon SageMaker to build, train, & deploy your ML Models
Using Amazon SageMaker to build, train, & deploy your ML ModelsAmazon Web Services
 
Accelerate your Machine Learning workflows with Amazon SageMaker
Accelerate your Machine Learning workflows with Amazon SageMakerAccelerate your Machine Learning workflows with Amazon SageMaker
Accelerate your Machine Learning workflows with Amazon SageMakerJulien SIMON
 
Picking the right AWS backend for your Java application
Picking the right AWS backend for your Java applicationPicking the right AWS backend for your Java application
Picking the right AWS backend for your Java applicationJulien SIMON
 

La actualidad más candente (20)

Machine Learning Models with Apache MXNet and AWS Fargate
Machine Learning Models with Apache MXNet and AWS FargateMachine Learning Models with Apache MXNet and AWS Fargate
Machine Learning Models with Apache MXNet and AWS Fargate
 
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...
 
ECS for Amazon Deep Learning and Amazon Machine Learning
ECS for Amazon Deep Learning and Amazon Machine LearningECS for Amazon Deep Learning and Amazon Machine Learning
ECS for Amazon Deep Learning and Amazon Machine Learning
 
Machine Learning in Action
Machine Learning in ActionMachine Learning in Action
Machine Learning in Action
 
FPGAs in the cloud? (October 2017)
FPGAs in the cloud? (October 2017)FPGAs in the cloud? (October 2017)
FPGAs in the cloud? (October 2017)
 
Scalable Deep Learning on AWS with Apache MXNet
Scalable Deep Learning on AWS with Apache MXNetScalable Deep Learning on AWS with Apache MXNet
Scalable Deep Learning on AWS with Apache MXNet
 
Deep Learning with Apache MXNet (September 2017)
Deep Learning with Apache MXNet (September 2017)Deep Learning with Apache MXNet (September 2017)
Deep Learning with Apache MXNet (September 2017)
 
Build, Train & Deploy Machine Learning Models at Scale
Build, Train & Deploy Machine Learning Models at ScaleBuild, Train & Deploy Machine Learning Models at Scale
Build, Train & Deploy Machine Learning Models at Scale
 
Build, Train, and Deploy ML Models at Scale
Build, Train, and Deploy ML Models at ScaleBuild, Train, and Deploy ML Models at Scale
Build, Train, and Deploy ML Models at Scale
 
Optimize your Machine Learning Workloads on AWS (July 2019)
Optimize your Machine Learning Workloads on AWS (July 2019)Optimize your Machine Learning Workloads on AWS (July 2019)
Optimize your Machine Learning Workloads on AWS (July 2019)
 
Build, train, and deploy ML models at scale.pdf
Build, train, and deploy ML models at scale.pdfBuild, train, and deploy ML models at scale.pdf
Build, train, and deploy ML models at scale.pdf
 
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...
 
Intel and Amazon - Powering your innovation together.
Intel and Amazon - Powering your innovation together. Intel and Amazon - Powering your innovation together.
Intel and Amazon - Powering your innovation together.
 
Using Amazon SageMaker to build, train, and deploy your ML Models
Using Amazon SageMaker to build, train, and deploy your ML ModelsUsing Amazon SageMaker to build, train, and deploy your ML Models
Using Amazon SageMaker to build, train, and deploy your ML Models
 
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMaker
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMakerAWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMaker
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMaker
 
Scalable Deep Learning on AWS using Apache MXNet (May 2017)
Scalable Deep Learning on AWS using Apache MXNet (May 2017)Scalable Deep Learning on AWS using Apache MXNet (May 2017)
Scalable Deep Learning on AWS using Apache MXNet (May 2017)
 
What Can HPC on AWS Do?
What Can HPC on AWS Do?What Can HPC on AWS Do?
What Can HPC on AWS Do?
 
Using Amazon SageMaker to build, train, & deploy your ML Models
Using Amazon SageMaker to build, train, & deploy your ML ModelsUsing Amazon SageMaker to build, train, & deploy your ML Models
Using Amazon SageMaker to build, train, & deploy your ML Models
 
Accelerate your Machine Learning workflows with Amazon SageMaker
Accelerate your Machine Learning workflows with Amazon SageMakerAccelerate your Machine Learning workflows with Amazon SageMaker
Accelerate your Machine Learning workflows with Amazon SageMaker
 
Picking the right AWS backend for your Java application
Picking the right AWS backend for your Java applicationPicking the right AWS backend for your Java application
Picking the right AWS backend for your Java application
 

Similar a Deep Learning at the Edge with AWS

Machine Learning Inference at the Edge
Machine Learning Inference at the EdgeMachine Learning Inference at the Edge
Machine Learning Inference at the EdgeAmazon Web Services
 
DataPalooza - A Music Festival themed ML + IoT Workshop
DataPalooza - A Music Festival themed ML + IoT WorkshopDataPalooza - A Music Festival themed ML + IoT Workshop
DataPalooza - A Music Festival themed ML + IoT WorkshopAmazon Web Services
 
Deep Learning with Apache MXNet
Deep Learning with Apache MXNetDeep Learning with Apache MXNet
Deep Learning with Apache MXNetJulien SIMON
 
Datapalooza: A Music Festival Themed ML & IoT Workshop
Datapalooza: A Music Festival Themed ML & IoT WorkshopDatapalooza: A Music Festival Themed ML & IoT Workshop
Datapalooza: A Music Festival Themed ML & IoT WorkshopAmazon Web Services
 
High Performance Computing in AWS, Immersion Day Huntsville 2019
High Performance Computing in AWS, Immersion Day Huntsville 2019High Performance Computing in AWS, Immersion Day Huntsville 2019
High Performance Computing in AWS, Immersion Day Huntsville 2019Amazon Web Services
 
AWS reinvent 2019 recap - Riyadh - Containers and Serverless - Paul Maddox
AWS reinvent 2019 recap - Riyadh - Containers and Serverless - Paul MaddoxAWS reinvent 2019 recap - Riyadh - Containers and Serverless - Paul Maddox
AWS reinvent 2019 recap - Riyadh - Containers and Serverless - Paul MaddoxAWS Riyadh User Group
 
Enabling Deep Learning in IoT Applications with Apache MXNet
Enabling Deep Learning in IoT Applications with Apache MXNetEnabling Deep Learning in IoT Applications with Apache MXNet
Enabling Deep Learning in IoT Applications with Apache MXNetAmazon Web Services
 
AI Scalability for the Next Decade
AI Scalability for the Next DecadeAI Scalability for the Next Decade
AI Scalability for the Next DecadePaula Koziol
 
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)Amazon Web Services
 
OS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of MLOS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of MLNordic APIs
 
Innovation with ai at scale on the edge vt sept 2019 v0
Innovation with ai at scale on the edge vt sept 2019 v0Innovation with ai at scale on the edge vt sept 2019 v0
Innovation with ai at scale on the edge vt sept 2019 v0Ganesan Narayanasamy
 
Achieving AI @scale on Mobile Devices
Achieving AI @scale on Mobile DevicesAchieving AI @scale on Mobile Devices
Achieving AI @scale on Mobile DevicesQualcomm Research
 
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra
 
AWS re:Invent 2016 recap (part 1)
AWS re:Invent 2016 recap (part 1)AWS re:Invent 2016 recap (part 1)
AWS re:Invent 2016 recap (part 1)Julien SIMON
 
Backend.AI Technical Introduction (19.09 / 2019 Autumn)
Backend.AI Technical Introduction (19.09 / 2019 Autumn)Backend.AI Technical Introduction (19.09 / 2019 Autumn)
Backend.AI Technical Introduction (19.09 / 2019 Autumn)Lablup Inc.
 

Similar a Deep Learning at the Edge with AWS (20)

Machine Learning Inference at the Edge
Machine Learning Inference at the EdgeMachine Learning Inference at the Edge
Machine Learning Inference at the Edge
 
DataPalooza - A Music Festival themed ML + IoT Workshop
DataPalooza - A Music Festival themed ML + IoT WorkshopDataPalooza - A Music Festival themed ML + IoT Workshop
DataPalooza - A Music Festival themed ML + IoT Workshop
 
DataPalooza: ML & IoT Workshop
DataPalooza: ML & IoT WorkshopDataPalooza: ML & IoT Workshop
DataPalooza: ML & IoT Workshop
 
Deep Learning with Apache MXNet
Deep Learning with Apache MXNetDeep Learning with Apache MXNet
Deep Learning with Apache MXNet
 
Datapalooza: A Music Festival Themed ML & IoT Workshop
Datapalooza: A Music Festival Themed ML & IoT WorkshopDatapalooza: A Music Festival Themed ML & IoT Workshop
Datapalooza: A Music Festival Themed ML & IoT Workshop
 
High Performance Computing in AWS, Immersion Day Huntsville 2019
High Performance Computing in AWS, Immersion Day Huntsville 2019High Performance Computing in AWS, Immersion Day Huntsville 2019
High Performance Computing in AWS, Immersion Day Huntsville 2019
 
Amazon Deep Learning
Amazon Deep LearningAmazon Deep Learning
Amazon Deep Learning
 
AWS reinvent 2019 recap - Riyadh - Containers and Serverless - Paul Maddox
AWS reinvent 2019 recap - Riyadh - Containers and Serverless - Paul MaddoxAWS reinvent 2019 recap - Riyadh - Containers and Serverless - Paul Maddox
AWS reinvent 2019 recap - Riyadh - Containers and Serverless - Paul Maddox
 
Enabling Deep Learning in IoT Applications with Apache MXNet
Enabling Deep Learning in IoT Applications with Apache MXNetEnabling Deep Learning in IoT Applications with Apache MXNet
Enabling Deep Learning in IoT Applications with Apache MXNet
 
AI Scalability for the Next Decade
AI Scalability for the Next DecadeAI Scalability for the Next Decade
AI Scalability for the Next Decade
 
Innovate on Cloud with AWS
Innovate on Cloud with AWSInnovate on Cloud with AWS
Innovate on Cloud with AWS
 
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)
 
BSC LMS DDL
BSC LMS DDL BSC LMS DDL
BSC LMS DDL
 
2020 re:Cap
2020 re:Cap2020 re:Cap
2020 re:Cap
 
OS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of MLOS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of ML
 
Innovation with ai at scale on the edge vt sept 2019 v0
Innovation with ai at scale on the edge vt sept 2019 v0Innovation with ai at scale on the edge vt sept 2019 v0
Innovation with ai at scale on the edge vt sept 2019 v0
 
Achieving AI @scale on Mobile Devices
Achieving AI @scale on Mobile DevicesAchieving AI @scale on Mobile Devices
Achieving AI @scale on Mobile Devices
 
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...
 
AWS re:Invent 2016 recap (part 1)
AWS re:Invent 2016 recap (part 1)AWS re:Invent 2016 recap (part 1)
AWS re:Invent 2016 recap (part 1)
 
Backend.AI Technical Introduction (19.09 / 2019 Autumn)
Backend.AI Technical Introduction (19.09 / 2019 Autumn)Backend.AI Technical Introduction (19.09 / 2019 Autumn)
Backend.AI Technical Introduction (19.09 / 2019 Autumn)
 

Más de Julien SIMON

An introduction to computer vision with Hugging Face
An introduction to computer vision with Hugging FaceAn introduction to computer vision with Hugging Face
An introduction to computer vision with Hugging FaceJulien SIMON
 
Reinventing Deep Learning
 with Hugging Face Transformers
Reinventing Deep Learning
 with Hugging Face TransformersReinventing Deep Learning
 with Hugging Face Transformers
Reinventing Deep Learning
 with Hugging Face TransformersJulien SIMON
 
Building NLP applications with Transformers
Building NLP applications with TransformersBuilding NLP applications with Transformers
Building NLP applications with TransformersJulien SIMON
 
Building Machine Learning Models Automatically (June 2020)
Building Machine Learning Models Automatically (June 2020)Building Machine Learning Models Automatically (June 2020)
Building Machine Learning Models Automatically (June 2020)Julien SIMON
 
Starting your AI/ML project right (May 2020)
Starting your AI/ML project right (May 2020)Starting your AI/ML project right (May 2020)
Starting your AI/ML project right (May 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)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)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...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)AIM361 Optimizing machine learning models with Amazon SageMaker (December 2019)
AIM361 Optimizing machine learning models with Amazon SageMaker (December 2019)Julien SIMON
 
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...Julien SIMON
 
A pragmatic introduction to natural language processing models (October 2019)
A pragmatic introduction to natural language processing models (October 2019)A pragmatic introduction to natural language processing models (October 2019)
A pragmatic introduction to natural language processing models (October 2019)Julien SIMON
 
Building smart applications with AWS AI services (October 2019)
Building smart applications with AWS AI services (October 2019)Building smart applications with AWS AI services (October 2019)
Building smart applications with AWS AI services (October 2019)Julien SIMON
 
Build, train and deploy ML models with SageMaker (October 2019)
Build, train and deploy ML models with SageMaker (October 2019)Build, train and deploy ML models with SageMaker (October 2019)
Build, train and deploy ML models with SageMaker (October 2019)Julien SIMON
 
The Future of AI (September 2019)
The Future of AI (September 2019)The Future of AI (September 2019)
The Future of AI (September 2019)Julien SIMON
 
Building Machine Learning Inference Pipelines at Scale (July 2019)
Building Machine Learning Inference Pipelines at Scale (July 2019)Building Machine Learning Inference Pipelines at Scale (July 2019)
Building Machine Learning Inference Pipelines at Scale (July 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...Train and Deploy Machine Learning Workloads with AWS Container Services (July...
Train and Deploy Machine Learning Workloads with AWS Container Services (July...Julien SIMON
 
Deep Learning on Amazon Sagemaker (July 2019)
Deep Learning on Amazon Sagemaker (July 2019)Deep Learning on Amazon Sagemaker (July 2019)
Deep Learning on Amazon Sagemaker (July 2019)Julien SIMON
 
Automate your Amazon SageMaker Workflows (July 2019)
Automate your Amazon SageMaker Workflows (July 2019)Automate your Amazon SageMaker Workflows (July 2019)
Automate your Amazon SageMaker Workflows (July 2019)Julien SIMON
 
Build, train and deploy ML models with Amazon SageMaker (May 2019)
Build, train and deploy ML models with Amazon SageMaker (May 2019)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)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
 

Más de Julien SIMON (20)

An introduction to computer vision with Hugging Face
An introduction to computer vision with Hugging FaceAn 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 TransformersReinventing Deep Learning
 with Hugging Face Transformers
Reinventing Deep Learning
 with Hugging Face Transformers
 
Building NLP applications with Transformers
Building NLP applications with TransformersBuilding NLP applications with Transformers
Building NLP applications with Transformers
 
Building Machine Learning Models Automatically (June 2020)
Building Machine Learning Models Automatically (June 2020)Building Machine Learning Models Automatically (June 2020)
Building Machine Learning Models Automatically (June 2020)
 
Starting your AI/ML project right (May 2020)
Starting your AI/ML project right (May 2020)Starting your AI/ML project right (May 2020)
Starting your AI/ML project right (May 2020)
 
Scale Machine Learning from zero to millions of users (April 2020)
Scale Machine Learning from zero to millions of users (April 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)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...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)AIM361 Optimizing machine learning models with Amazon SageMaker (December 2019)
AIM361 Optimizing machine learning models with Amazon SageMaker (December 2019)
 
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...
 
A pragmatic introduction to natural language processing models (October 2019)
A pragmatic introduction to natural language processing models (October 2019)A pragmatic introduction to natural language processing models (October 2019)
A pragmatic introduction to natural language processing models (October 2019)
 
Building smart applications with AWS AI services (October 2019)
Building smart applications with AWS AI services (October 2019)Building smart applications with AWS AI services (October 2019)
Building smart applications with AWS AI services (October 2019)
 
Build, train and deploy ML models with SageMaker (October 2019)
Build, train and deploy ML models with SageMaker (October 2019)Build, train and deploy ML models with SageMaker (October 2019)
Build, train and deploy ML models with SageMaker (October 2019)
 
The Future of AI (September 2019)
The Future of AI (September 2019)The Future of AI (September 2019)
The Future of AI (September 2019)
 
Building Machine Learning Inference Pipelines at Scale (July 2019)
Building Machine Learning Inference Pipelines at Scale (July 2019)Building Machine Learning Inference Pipelines at Scale (July 2019)
Building Machine Learning Inference Pipelines at Scale (July 2019)
 
Train and Deploy Machine Learning Workloads with AWS Container Services (July...
Train and Deploy Machine Learning Workloads with AWS Container Services (July...Train and Deploy Machine Learning Workloads with AWS Container Services (July...
Train and Deploy Machine Learning Workloads with AWS Container Services (July...
 
Deep Learning on Amazon Sagemaker (July 2019)
Deep Learning on Amazon Sagemaker (July 2019)Deep Learning on Amazon Sagemaker (July 2019)
Deep Learning on Amazon Sagemaker (July 2019)
 
Automate your Amazon SageMaker Workflows (July 2019)
Automate your Amazon SageMaker Workflows (July 2019)Automate your Amazon SageMaker Workflows (July 2019)
Automate your Amazon SageMaker Workflows (July 2019)
 
Build, train and deploy ML models with Amazon SageMaker (May 2019)
Build, train and deploy ML models with Amazon SageMaker (May 2019)Build, train and deploy ML models with Amazon SageMaker (May 2019)
Build, train and deploy ML models with Amazon SageMaker (May 2019)
 
Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)
Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)
Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)
 

Último

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
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Alan Dix
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersThousandEyes
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Paola De la Torre
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?XfilesPro
 

Último (20)

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...
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?
 

Deep Learning at the Edge with AWS

  • 1. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Julien Simon, Principal Evangelist, AI & Machine Learning @julsimon Deep Learning at the Edge
  • 2. Deep Learning at the Edge 1. Flexible experimentation in the Cloud. 2. Scalable training in the Cloud. 3. Good prediction performance at the Edge. 4. Simple deployment of code and model
  • 3. Flexible experimentation in the Cloud • Apache MXNet: Python, R, Perl, Matlab, Scala, C++. • Gluon • Imperative programming aka ‘define-by-run’. • Inspect, debug and modify models during training. • Extensive model zoo • Pre-trained computer vision models. • MobileNet, SqueezeNet for resource-constrained devices.
  • 4. Scalable training in the Cloud Amazon SageMaker AWS Deep Learning AMI Amazon EC2 c5 p3
  • 5. Good prediction performance at the Edge • MXNet is written in C++. • Gluon networks can be ‘hybridized’ for additional speed. • Two libraries boost performance on CPU-only devices • Fast implementation of math primitives • Hardware-specific instructions, e.g. Intel AVX or ARM NEON • Intel Math Kernel Library https://software.intel.com/en-us/mkl • NNPACK https://github.com/Maratyszcza/NNPACK • Mixed precision training • Use float16 instead of float32 for weights and activations • Almost 2x reduction in model size, no loss of accuracy, faster inference • https://devblogs.nvidia.com/parallelforall/mixed-precision-training-deep-neural-networks/
  • 6. Simple deployment of code and model • Train a model in SageMaker (or bring your own). • Write a Lambda function performing prediction. • Add both as resources in your Greengrass group. • Let Greengrass handle deployment and updates. Best when You want the same programming model in the Cloud and at the Edge. Code and models need to be updated, even if network connectivity is infrequent or unreliable. One device in the group should be able to perform prediction on behalf on other devices. Requirements Devices are powerful enough to run Greengrass (XXX HW requirements) Devices are provisioned in AWS IoT (certificate, keys).
  • 7. ML Inference using AWS Greengrass Deploy model and Lambda separately Send insights Predict and take actions locally on device AWS Cloud
  • 8. AWS DeepLens Intel Atom CPU Gen9 graphics Ubuntu 16.04 LTS 100 GFLOPS performance Dual band Wi-Fi 8 GB RAM 16 GB Storage (eMMC) 32 GB SD card 4 MP camera with MJPEG H.264 encoding at 1080p resolution 2 USB ports Micro HDMI Audio out AWS Greengrass preconfigured Intel clDNN for Apache MXNet
  • 9. AWS DeepLens Architecture Video out Data out D E P L O Y P R O J E C T S Manage device Console Project Management I N F E R E N C E AWS Cloud Intel Model Optimizer
  • 11. Object detection with AWS DeepLens
  • 13. Resources https://mxnet.incubator.apache.org http://gluon.mxnet.io https://aws.amazon.com/sagemaker (free tier available) An overview of Amazon SageMaker: https://www.youtube.com/watch?v=ym7NEYEx9x4 https://github.com/awslabs/amazon-sagemaker-examples https://aws.amazon.com/greengrass (free tier available) https://aws.amazon.com/deeplens https://github.com/intel/clDNN https://medium.com/@julsimon You’ll pay for this.
  • 14. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Thank you! Julien Simon, Principal Evangelist, AI & Machine Learning @julsimon