DataPalooza at the San Francisco Loft: In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
AWS re:Invent 2016: Internet of Things (IoT) Edge and Device Services (IOT202)Amazon Web Services
AWS IoT edge and device services make it easy to get started and scale quickly along with your business needs. Medical equipment, industrial machinery, building automation, and simple device to trigger services, are just a few physical-world use cases that are benefiting from elastic cloud computing while meeting the local execution requirements and real time responsiveness. This session covers the intersection between the device and cloud industries, and the way AWS and our customers will shape the future of those industries together. We will showcase how our customers are using AWS IoT Button, the IoT Device SDKs, and other AWS services to improve the existing business models, invent new way of working, and balance the benefits of the cloud services with the need for local execution.
AWS re:Invent 2016: Transforming Industrial Processes with Deep Learning (MAC...Amazon Web Services
Deep learning has revolutionized computer vision by significantly increasing the accuracy of recognition systems. This session will discuss how the Amazon Fulfillment Technologies Computer Vision Research team has harnessed deep learning to identify inventory defects in Amazon’s warehouses. Beginning with a brief overview of how orders on Amazon.com are fulfilled, the talk will describe a combination of hardware and software that uses computer vision and deep learning that visually examine bins of Amazon inventory to locate possible mismatches between the physical inventory and inventory records. With the growth of deep learning, the emphasis of new system design shifts from clever algorithms to innovative ways to harness available data.
AWS re:Invent 2016: IoT and Beyond: Building IoT Solutions for Exploring the ...Amazon Web Services
Jet Propulsion Laboratory is a well-known innovator in outer space, particularly in its search for "life out there". JPL is now innovating in the physical space to improve “life here". AWS IoT is critical to their innovations. See a re:Invent preview about how JPL, as an early adopter of AWS IoT, has prototyped voice control to ask questions of the room, the budget, or the system. They’ve also used it for controlling lights and sound to detect cyber security threats, rapid prototyping of robots, low-cost virtual windows to the outside, and much more. The results have been excellent. JPL will demonstrate and talk about these prototypes, including what worked and what didn’t. They will also share the promise integrated serverless computing holds.
In this general session, AWS IoT experts will present an in-depth look at the current state of the Internet of Things. Learn about trends and industry use cases. Hear how other organizations are using AWS IoT to connect devices to the cloud. Explore some of the most recent IoT announcements as we kick off the IoT re:Source Mini Con.
In recent months, Artificial Intelligence has become the hottest topic in the IT industry. Of course, this has happened before, often with disappointing results: in this talk, we’ll explain why it is different this time.
The IoT is here to stay. As with any other trend in the history of computer software, it’s starting to produce a new generation of cloud platforms. This tech talk will identify and explain what to look for when evaluating an IoT cloud platform to ensure a successful deployment of IoT strategies.
In this hands-on workshop, the idea is that we provide attendees with sample code to simulate telemetry from devices (Wind Turbines), sample training data to train the machine learning models. AWS IoT rules engine will use machine learning for predicting failures and control the device when failure is predicted.
AWS re:Invent 2016: Internet of Things (IoT) Edge and Device Services (IOT202)Amazon Web Services
AWS IoT edge and device services make it easy to get started and scale quickly along with your business needs. Medical equipment, industrial machinery, building automation, and simple device to trigger services, are just a few physical-world use cases that are benefiting from elastic cloud computing while meeting the local execution requirements and real time responsiveness. This session covers the intersection between the device and cloud industries, and the way AWS and our customers will shape the future of those industries together. We will showcase how our customers are using AWS IoT Button, the IoT Device SDKs, and other AWS services to improve the existing business models, invent new way of working, and balance the benefits of the cloud services with the need for local execution.
AWS re:Invent 2016: Transforming Industrial Processes with Deep Learning (MAC...Amazon Web Services
Deep learning has revolutionized computer vision by significantly increasing the accuracy of recognition systems. This session will discuss how the Amazon Fulfillment Technologies Computer Vision Research team has harnessed deep learning to identify inventory defects in Amazon’s warehouses. Beginning with a brief overview of how orders on Amazon.com are fulfilled, the talk will describe a combination of hardware and software that uses computer vision and deep learning that visually examine bins of Amazon inventory to locate possible mismatches between the physical inventory and inventory records. With the growth of deep learning, the emphasis of new system design shifts from clever algorithms to innovative ways to harness available data.
AWS re:Invent 2016: IoT and Beyond: Building IoT Solutions for Exploring the ...Amazon Web Services
Jet Propulsion Laboratory is a well-known innovator in outer space, particularly in its search for "life out there". JPL is now innovating in the physical space to improve “life here". AWS IoT is critical to their innovations. See a re:Invent preview about how JPL, as an early adopter of AWS IoT, has prototyped voice control to ask questions of the room, the budget, or the system. They’ve also used it for controlling lights and sound to detect cyber security threats, rapid prototyping of robots, low-cost virtual windows to the outside, and much more. The results have been excellent. JPL will demonstrate and talk about these prototypes, including what worked and what didn’t. They will also share the promise integrated serverless computing holds.
In this general session, AWS IoT experts will present an in-depth look at the current state of the Internet of Things. Learn about trends and industry use cases. Hear how other organizations are using AWS IoT to connect devices to the cloud. Explore some of the most recent IoT announcements as we kick off the IoT re:Source Mini Con.
In recent months, Artificial Intelligence has become the hottest topic in the IT industry. Of course, this has happened before, often with disappointing results: in this talk, we’ll explain why it is different this time.
The IoT is here to stay. As with any other trend in the history of computer software, it’s starting to produce a new generation of cloud platforms. This tech talk will identify and explain what to look for when evaluating an IoT cloud platform to ensure a successful deployment of IoT strategies.
In this hands-on workshop, the idea is that we provide attendees with sample code to simulate telemetry from devices (Wind Turbines), sample training data to train the machine learning models. AWS IoT rules engine will use machine learning for predicting failures and control the device when failure is predicted.
AWS re:Invent 2016: Enel E2E Smart Home Solution with Amazon Alexa (IOT308)Amazon Web Services
Simply connecting the “things” that were never connected before as part of the Internet-of-Things is leading to new data insights that translate into meaningful change. AWS and Intel are working together to provide a secure, scalable edge-to-cloud solution for IoT applications. Intel gateways utilize Windriver Helix Device Cloud to authenticate the device with AWS IoT and initiate secure data transport as well as provide a framework for active edge device management and over the air software and security updates. With AWS and Intel, you can implement an IoT solution quickly and with minimal upfront investments, seamless connectivity, and deliver enhanced security from device to network to cloud, then use AWS Big Data services to drive business insight.
In this workshop you will learn how to use the Amazon Alexa Skills Kit SDK along with the Windriver Helix Device Cloud API to teach Alexa
- how to pass requests and data between Alexa and an Intel IoT Gateway based solution
- how to provision IoT edge devices, sensors and smart switches as part of an End-to-End SmartHome solution.
We will guide you through the scripting and code snippets required to do this. We will provide you with a real live experience of making Amazon Alexa and Windriver Helix Device Cloud device management work together and testing the flexible Smart Home reference framework.
The workshop is based on a ready-to-deploy smart home and energy monitoring solution for which Enel partnered with Intel and AWS to realize real energy cost savings and quality of home life improvements for their customers. Enel is an Italian utility company serving over sixty million households. They turned to Intel because it delivers an ecosystem of suppliers and unmatched security and data ingestion scalability from edge to AWS cloud.
AMF304-Optimizing Design and Engineering Performance in the Cloud for Manufac...Amazon Web Services
Manufacturing companies in all sectors—including automotive, aerospace, semiconductor, and industrial manufacturing—rely on design and engineering software in their product development processes. These computationally-intensive applications—such as computer-aided design and engineering (CAD and CAE), electronic design automation (EDA), other performance-critical applications—require immense scale and orchestration to meet the demands of today’s manufacturing requirements. In this session, you learn how to achieve the maximum possible performance and throughput from design and engineering workloads running on Amazon EC2, elastic GPUs, and managed services such as AWS Batch and Amazon AppStream 2.0. We demonstrate specific optimization techniques and share samples on how to accelerate batch and interactive workloads on AWS. We also demonstrate how to extend and migrate on-premises, high performance compute workloads with AWS, and use a combination of On-Demand Instances, Reserved Instances, and Spot Instances to minimize costs.
by Gavin Adams, IoT Specialist Solutions Architect AWS and Anton Shmagin, Partner Solutions Architect AWS
Join us for AWS IoT day at the AWS San Francisco Loft. AWS IoT enables you to easily connect and manage millions of devices securely. You can gather data from, run sophisticated analytics on, and take actions in real-time on your diverse fleet of IoT devices from edge to the cloud. You will build IoT applications with AWS IoT experts. AWS IoT provides edge-based software and cloud-based services so you can easily build IoT applications. Edge-based software, including AWS Greengrass, enables you to securely connect devices, gather data and take intelligent actions locally even when Internet connectivity is down. Cloud-based services, including AWS IoT Core, allow you to quickly onboard large and diverse fleets, maintain fleet health, and keep fleets secure.
Azure Digital Twins is a PaaS service to build IoT solution on the Azure platform focused on state and on a spatial intelligence graph to model the devices, the sensors and the environment around them.
In this session we talk aboutthe spatial graph and the pipeline the data follow from generation in the device to the processing.
Container Soup for Your Soul: The Microservice Edition, Building Deployment ...Amazon Web Services
The talk is the story of a Clever's journey to effectively use a container orchestration system (ECS) and a walk through decisions to create a simple and effective deployment pipeline. We will go through various aspects of building application deployment pipelines for microservices. Clever is an education technology company and we do hundreds of deployments of tens of thousands of containers every week to serve over 50% of K-12 public and private school districts in the US. Learn More: https://aws.amazon.com/government-education/
Getting Started with AWS Internet of Things - AWS Summit Cape Town 2017Amazon Web Services
AWS IoT is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. In this tech talk, we will discuss how constrained devices can leverage AWS IoT to send data to the cloud and receive commands back to the device from the cloud using the protocol of their choice. We will use the AWS IoT Starter Kit to demonstrate building a real connected product, securely connect with AWS IoT using MQTT, WebSockets, and HTTP protocols, and show how developers and businesses can leverage features of AWS IoT like Device Shadows and the Rules Engine, which provides message processing and integration with other AWS services.
AWS Speaker: Boaz Ziniman, Technical Evangelist - Amazon Web Services
Try Amazon cloud. Avail our 360 degree report on your Benefits / ROI / Migration Timeline exclusive for your business. Amazon Cloud can bring your maintenance/investment reduced, giving worlds efficient IT infrastructure that is required to meet your scaling up/down online software application/data access and processing speed for your business you have/use may get moved to Amazon cloud with all required compliances. We request an hour time slot with your stakeholders, next week, between Monday and Friday , 8am and 5pm EST.
Announcing AWS Greengrass - January 2017 AWS Online Tech TalksAmazon Web Services
AWS Greengrass is a new software platform for running local compute, data caching and messaging on connected devices. You can process device data locally, even if a device is temporarily disconnected.
Learning Objectives:
• Learn about the capabilities, features and benefits of AWS Greengrass
• Learn about the different use cases
• Learn how to get started using AWS Greengrass
Today's technical landscape features workloads that can no longer be accomplished on a single server using technology from years past. As a result, we must find new ways to accommodate the increasing demands on our compute performance. Some of these new strategies introduce trade-offs and additional complexity into a system.
In this presentation, we give an overview of scaling and how to address performance concerns that business are facing, today.
Did you know 52% of today’s organizations are planning to leverage a hybrid-cloud approach? With eight years’ experience running Windows workloads in the cloud, AWS provides the perfect platform to modernize your Microsoft applications.
This webinar will demonstrate how AWS ensures customization, high availability and scalability for most of your Microsoft applications on a hybrid-cloud model and learn how to reduce cost. We will also offer you an understanding of how these workloads are licensed and monitored, and share best practice reference architectures.
Key Outcomes:
• How to get the most out of your Microsoft Applications
• How do you start Migrating Applications to AWS?
• Hybrid cloud deployments using AWS
• Licensing Considerations
Session is suitable for
• Technical Decision Makers
• Senior IT Managers and Specialist
• DBA’s
• Solution Architects and Engineers
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...Amazon Web Services
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
AWS Greengrass is a new software platform for running local compute, data caching and messaging on connected devices. With AWS Greengrass, connected devices can run AWS Lambda functions, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet. Using AWS Lambda, Greengrass ensures your IoT devices can respond quickly to local events, operate with intermittent connections, and minimize the cost of transmitting IoT data to the cloud.
Cloud computing has become the new normal. In today's session we will explore why customers are choosing to migrate 'all in' to AWS. We cover the benefits and best practice for migration. You will hear from our customers on the decision making process they followed before moving to AWS. This session will cover the key considerations in assessing the opportunity, building the business case, project plan, selecting the right migration strategy, and partner selection.
Speaker: Paul Woodward, Account Manager, Amazon Web Services
Featured Customer - Cromwell Property Gorup
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun.
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
by Mahendra Bairagi, AI Specialist Solutions Architect, AWS
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun. You are looking to use Machine Learning and IoT technologies to solve this unique problem.
Do you accept the Challenge?
The objective of this task is to help the festival-goers quickly identify the DJ stage where crowd is the happiest. You've seen a lot of buzz around computer vision, machine learning, and IoT and want to use this technology to detect crowd emotions. From your initial research there are existing ML models that you can leverage to do face and emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will work the best for your needs at the festival? You are going to test both approaches and find out!
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
AWS re:Invent 2016: Enel E2E Smart Home Solution with Amazon Alexa (IOT308)Amazon Web Services
Simply connecting the “things” that were never connected before as part of the Internet-of-Things is leading to new data insights that translate into meaningful change. AWS and Intel are working together to provide a secure, scalable edge-to-cloud solution for IoT applications. Intel gateways utilize Windriver Helix Device Cloud to authenticate the device with AWS IoT and initiate secure data transport as well as provide a framework for active edge device management and over the air software and security updates. With AWS and Intel, you can implement an IoT solution quickly and with minimal upfront investments, seamless connectivity, and deliver enhanced security from device to network to cloud, then use AWS Big Data services to drive business insight.
In this workshop you will learn how to use the Amazon Alexa Skills Kit SDK along with the Windriver Helix Device Cloud API to teach Alexa
- how to pass requests and data between Alexa and an Intel IoT Gateway based solution
- how to provision IoT edge devices, sensors and smart switches as part of an End-to-End SmartHome solution.
We will guide you through the scripting and code snippets required to do this. We will provide you with a real live experience of making Amazon Alexa and Windriver Helix Device Cloud device management work together and testing the flexible Smart Home reference framework.
The workshop is based on a ready-to-deploy smart home and energy monitoring solution for which Enel partnered with Intel and AWS to realize real energy cost savings and quality of home life improvements for their customers. Enel is an Italian utility company serving over sixty million households. They turned to Intel because it delivers an ecosystem of suppliers and unmatched security and data ingestion scalability from edge to AWS cloud.
AMF304-Optimizing Design and Engineering Performance in the Cloud for Manufac...Amazon Web Services
Manufacturing companies in all sectors—including automotive, aerospace, semiconductor, and industrial manufacturing—rely on design and engineering software in their product development processes. These computationally-intensive applications—such as computer-aided design and engineering (CAD and CAE), electronic design automation (EDA), other performance-critical applications—require immense scale and orchestration to meet the demands of today’s manufacturing requirements. In this session, you learn how to achieve the maximum possible performance and throughput from design and engineering workloads running on Amazon EC2, elastic GPUs, and managed services such as AWS Batch and Amazon AppStream 2.0. We demonstrate specific optimization techniques and share samples on how to accelerate batch and interactive workloads on AWS. We also demonstrate how to extend and migrate on-premises, high performance compute workloads with AWS, and use a combination of On-Demand Instances, Reserved Instances, and Spot Instances to minimize costs.
by Gavin Adams, IoT Specialist Solutions Architect AWS and Anton Shmagin, Partner Solutions Architect AWS
Join us for AWS IoT day at the AWS San Francisco Loft. AWS IoT enables you to easily connect and manage millions of devices securely. You can gather data from, run sophisticated analytics on, and take actions in real-time on your diverse fleet of IoT devices from edge to the cloud. You will build IoT applications with AWS IoT experts. AWS IoT provides edge-based software and cloud-based services so you can easily build IoT applications. Edge-based software, including AWS Greengrass, enables you to securely connect devices, gather data and take intelligent actions locally even when Internet connectivity is down. Cloud-based services, including AWS IoT Core, allow you to quickly onboard large and diverse fleets, maintain fleet health, and keep fleets secure.
Azure Digital Twins is a PaaS service to build IoT solution on the Azure platform focused on state and on a spatial intelligence graph to model the devices, the sensors and the environment around them.
In this session we talk aboutthe spatial graph and the pipeline the data follow from generation in the device to the processing.
Container Soup for Your Soul: The Microservice Edition, Building Deployment ...Amazon Web Services
The talk is the story of a Clever's journey to effectively use a container orchestration system (ECS) and a walk through decisions to create a simple and effective deployment pipeline. We will go through various aspects of building application deployment pipelines for microservices. Clever is an education technology company and we do hundreds of deployments of tens of thousands of containers every week to serve over 50% of K-12 public and private school districts in the US. Learn More: https://aws.amazon.com/government-education/
Getting Started with AWS Internet of Things - AWS Summit Cape Town 2017Amazon Web Services
AWS IoT is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. In this tech talk, we will discuss how constrained devices can leverage AWS IoT to send data to the cloud and receive commands back to the device from the cloud using the protocol of their choice. We will use the AWS IoT Starter Kit to demonstrate building a real connected product, securely connect with AWS IoT using MQTT, WebSockets, and HTTP protocols, and show how developers and businesses can leverage features of AWS IoT like Device Shadows and the Rules Engine, which provides message processing and integration with other AWS services.
AWS Speaker: Boaz Ziniman, Technical Evangelist - Amazon Web Services
Try Amazon cloud. Avail our 360 degree report on your Benefits / ROI / Migration Timeline exclusive for your business. Amazon Cloud can bring your maintenance/investment reduced, giving worlds efficient IT infrastructure that is required to meet your scaling up/down online software application/data access and processing speed for your business you have/use may get moved to Amazon cloud with all required compliances. We request an hour time slot with your stakeholders, next week, between Monday and Friday , 8am and 5pm EST.
Announcing AWS Greengrass - January 2017 AWS Online Tech TalksAmazon Web Services
AWS Greengrass is a new software platform for running local compute, data caching and messaging on connected devices. You can process device data locally, even if a device is temporarily disconnected.
Learning Objectives:
• Learn about the capabilities, features and benefits of AWS Greengrass
• Learn about the different use cases
• Learn how to get started using AWS Greengrass
Today's technical landscape features workloads that can no longer be accomplished on a single server using technology from years past. As a result, we must find new ways to accommodate the increasing demands on our compute performance. Some of these new strategies introduce trade-offs and additional complexity into a system.
In this presentation, we give an overview of scaling and how to address performance concerns that business are facing, today.
Did you know 52% of today’s organizations are planning to leverage a hybrid-cloud approach? With eight years’ experience running Windows workloads in the cloud, AWS provides the perfect platform to modernize your Microsoft applications.
This webinar will demonstrate how AWS ensures customization, high availability and scalability for most of your Microsoft applications on a hybrid-cloud model and learn how to reduce cost. We will also offer you an understanding of how these workloads are licensed and monitored, and share best practice reference architectures.
Key Outcomes:
• How to get the most out of your Microsoft Applications
• How do you start Migrating Applications to AWS?
• Hybrid cloud deployments using AWS
• Licensing Considerations
Session is suitable for
• Technical Decision Makers
• Senior IT Managers and Specialist
• DBA’s
• Solution Architects and Engineers
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...Amazon Web Services
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
AWS Greengrass is a new software platform for running local compute, data caching and messaging on connected devices. With AWS Greengrass, connected devices can run AWS Lambda functions, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet. Using AWS Lambda, Greengrass ensures your IoT devices can respond quickly to local events, operate with intermittent connections, and minimize the cost of transmitting IoT data to the cloud.
Cloud computing has become the new normal. In today's session we will explore why customers are choosing to migrate 'all in' to AWS. We cover the benefits and best practice for migration. You will hear from our customers on the decision making process they followed before moving to AWS. This session will cover the key considerations in assessing the opportunity, building the business case, project plan, selecting the right migration strategy, and partner selection.
Speaker: Paul Woodward, Account Manager, Amazon Web Services
Featured Customer - Cromwell Property Gorup
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun.
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
by Mahendra Bairagi, AI Specialist Solutions Architect, AWS
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun. You are looking to use Machine Learning and IoT technologies to solve this unique problem.
Do you accept the Challenge?
The objective of this task is to help the festival-goers quickly identify the DJ stage where crowd is the happiest. You've seen a lot of buzz around computer vision, machine learning, and IoT and want to use this technology to detect crowd emotions. From your initial research there are existing ML models that you can leverage to do face and emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will work the best for your needs at the festival? You are going to test both approaches and find out!
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
Machine Learning Inference at the EdgeJulien SIMON
Machine Learning works by using powerful algorithms to discover patterns in data and construct complex mathematical models using these patterns. Once the model is built, you perform inference by applying new data to the trained model to make predictions for your application. Building and training ML models require massive computing resources so it is a natural fit for the cloud. But, inference takes a lot less computing power and is typically done in real-time when new data is available, so getting inference results with very low latency is important to making sure your applications can respond quickly to local events. AWS Greengrass ML inference gives you the best of both worlds. You use ML models that are built and trained in the cloud and you deploy and run ML inference locally on connected devices. For example, autonomous cars need to identify road signs in real time, drones need to recognize objects with or without network connectivity.
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.
"Do you want to learn more about building predictive IoT applications using AWS IoT and Amazon Machine Learning (Amazon ML)? In this workshop, we walk step by step through configuring AWS IoT “things”, training machine learning models using Amazon ML, and then using those models with AWS Lambda to predict device failures in the field and take corrective action. This is a hands-on workshop that provides participants with all of the code and machine learning training data needed to build a fully functional real-world IoT simulation. Participants should have a basic familiarity with AWS and with using the AWS Management Console.
This workshop is hands-on and provides the participants with all of the code and machine learning training data necessary to build a fully functional real-world IoT simulation.
Participants should have a basic familiarity with AWS and be familiar with using the console."
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra
Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
The session presented a perspective how INTEL Cloud Solutions enables a deployment model that is workload optimized to every application
Speaker: Kavitha Mohammad,Director Industry Solutions Group, Intel
Building what's next with google cloud's powerful infrastructureMediaAgility
Building What's Next with Google Cloud's Powerful Infrastructure. Companies are facing increasing challenges
Be more data driven, but on-prem data is hard to access, analyze, and use
Have to focus to stay ahead of competition, can’t afford wasted efforts
Attract and retain customers and employees with great experiences
Security threats keep growing
Be more agile - turn IT into competitive advantage
Google is focused on helping companies meet those challenges. To know more feel free to explore these slides and write back to us.
View these slides if you're you new to cloud computing and would like to learn more about Amazon Web Services (AWS), if you intend to implement a project and would like to discover the basics of the AWS cloud or if you are a business looking to evaluate cloud computing, attend this webinar. In this recorded webinar, we answer the following questions:
• What is Cloud Computing with AWS and what benefits can it deliver?
• Who is using AWS and what are they using it for?
• How can I use AWS Services to run my workloads?
View the webinar: http://youtu.be/ybcV0sJ_T_I
Understand the core concepts of “Cloud Computing” and how businesses around the world are running the infrastructure that supports their websites to lower costs, improve time-to-market, and enable rapid scalability matching resource to demands of users. Whether you are an enterprise looking for IT innovation, agility and resiliency or small and medium business who wants to accelerate growth without a big upfront investment in cash or time for technology, the AWS Cloud provides a complete set of services at zero upfront costs which are available with a few clicks and within minutes.
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
3. The Challenge
DataPalooza—A music festival themed ML & IoT Workshop
Scenario: Your bold startup has taken the challenge of providing a new type of EDM music festival
experience. At venues with multiple stages, festival-goers are always looking to identify which DJ stage
areas are the liveliest. This causes them to constantly move around between different stages and miss
out. You are looking to use Machine Learning and IoT to come up with a connected
fan experience that takes the music festival scene to the next level. From your initial research there are
existing ML models that you can leverage to do face and emotion detection, but there are two ways
that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will
work the best for your needs at the festival? You are going to test both approaches and find out!
In this workshop you will use AWS and Intel technologies including Amazon SageMaker with Intel C5
Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, AWS Lambda, along with Intel IoT
hardware kits. The objective of the workshop is to learn how to build and deploy a machine learning
model and then run inference on it from the cloud and from the edge device.
By the time you’re done with these challenges, EDM DJ’s will be able to tell whether the crowd is
enjoying their set by the looks on their faces.
6. Machine Learning Process is Hard…
Fetch
data
Clean &
format data
Prepare &
transform
data
Train
model
Evaluate
model
Integrate
with prod
Monitor/
debug/
refresh
Data wrangling
• Set up and manage
Notebook environments
• Get data to notebooks securely
Experimentation
• Set up and manage clusters
• Scale/distribute ML algorithms
Deployment
• Set up and manage
inference clusters
• Manage and auto scale
inference APIs
• Testing, versioning,
and monitoring
7. Amazon’s fast, scalable algorithms
Distributed Apache MXNet and TensorFlow
Bring your own algorithm
Hyperparameter optimization
Building HostingTraining
Amazon SageMaker Components
8. Amazon SageMaker Components
Amazon’s fast, scalable algorithms
Distributed Apache MXNet and TensorFlow
Bring your own algorithm
Hyperparameter optimization
Building Hosting (C/P)Training
9. Resizable as
you need
Common tools
pre-installed
Easy access to
your data sources
No servers
to manage
Zero Setup for Data Exploration
10. Amazon SageMaker Components
Amazon’s fast, scalable algorithms
Distributed Apache MXNet and TensorFlow
Bring your own algorithm
Hyperparameter optimization
Building Hosting (C/P)Training
Clusters of GPU
or powerful CPU
11. Distributed Training that Works with You
Amazon-optimized
algorithms using the
AWS SDK…
… or Apache Spark
IM Estimators
Bring your own deep
learning script…
… or your custom
algorithm Docker image
12. More than Just General Purpose Algorithms
XGBoost, FM, and
Linear for classification
and regression
Kmeans and PCA
for clustering and
dimensionality reduction
Image classification
with convolutional
neural networks
LDA and NTM for
topic modeling, seq2seq
for translation
13. Amazon ECS
Bring Your Own Algorithm
... publish to Amazon ECS... add algorithm code
to a Docker container...
Choose your own framework
14. Amazon’s fast, scalable algorithms
Distributed Apache MXNet and TensorFlow
Bring your own algorithm
Hyperparameter optimization
Building Hosting (C/P)Training
Elastic Clusters
CPU or GPU
instances
Amazon SageMaker Components
16. Modular Architecture So You Can Use What You Need
Training
algorithm
Model
artifacts
Inference
code
Client
application
Model
Data Inference
Ground
truth
Amazon SageMaker
Past
Data
17. Pay As You Go and Inexpensive
ML compute by the
second starting
at $0.0464/hr
ML storage by the
second at $0.14
per GB-month
Data processed in
notebooks and hosting
at $0.016 per GB
Free trial to
get started quickly
18. Amazon EC2 C5 Instances
Cost effective CPUs, e.g., for models using INT8
• Powered by 3.0 GHz Intel Xeon (Skylake) platinum processors
• 72 vCPUs and 155-GB RAM (25% price/performance improvement versus C4)
• Nitro Hypervisor for larger instance sizes
Ideal for running ML inference as GPU based instances would be overkill
(cost saving)
Suitable for training simple ML algorithms (text or CSV data) or during
dev/test mode and proof-of-concepts
19. Can we do more to put ML in the
hands of all developers (literally)?
20. AWS Deeplens
is not a video camera…
…it’s the worlds first
Deep Learning Enabled
Developer kit
25. AWS Deeplens Architecture
Video out
Data out
Inference
Deploy projects
Manage device
Security
Console Project
Management
AWS Cloud
Intel: Model Optimizer
cIDNN and Driver
27. AWS and Intel
Amazon Web Services (AWS) and Intel technologies are designed to provide
a more secure, scalable edge-to-cloud solution for IoT applications
• Operate locally and on the cloud
• Easily manage and update devices
• Connect fleets of devices, gateways, and cloud environments
28. Customer Pain Points with IoT Implementation
Security
• Securing data transport to the cloud
with encryption
• Enabling devices to communicate
with one another without
introducing vulnerabilities
• Ensuring devices have not been
tampered with before sending data
to the cloud
• Authenticating device identity without
sending credentials over the wire
Deployment and Management
• Managing large numbers of
simultaneous connections to devices
connecting via different networks
• Updating device software, patching, and
sending configurations to device fleets
• Incorporating legacy and proprietary
protocols with IoT deployments
• Bandwidth and storage costs of
sending device data to the cloud when
local hardware has sufficient resources
for local analytics
• Ongoing security management over
life of implementation
Scale
• Managing large numbers of
simultaneous connections to devices
connecting via different networks
• Updating device software, patching, and
sending configurations to device fleets
• Incorporating legacy and proprietary
protocols with IoT deployments
• Bandwidth and storage costs of
sending device data to the cloud when
local hardware has sufficient resources
for analytics
29. Benefits of Using AWS with Intel IoT Hardware
Easy to deploy
and manage
Whether making existing
things smart or deploying
new connected devices,
AWS and Intel make it easy
to get started
Security enabled
Intel hardware and software
solutions are tightly
integrated with the robust
AWS cloud infrastructure to
deliver enhanced security,
from to device, to network,
to cloud
Scalable
Start with minimal or no
upfront investment and
easily scale to millions
of devices and billions
of messages
Cost-effective
Leverage pay-as-you-go
pricing, the flexibility to use
local and cloud resources,
and flexible and low-cost IT
resources powered by Intel
technology to reduce the
costs of IoT deployments
30. AWS and Intel Strategies to Maximize Value
of IoT Deployments
Act locally on device data
at the edge. Use the cloud
for management, analytics,
and durable storage
Operate offline in
circumstances when
latency requirements or
intermittent connectivity
that make a round trip to
the cloud unfeasible
Execute AWS Lambda
functions locally using AWS
Greengrass, reducing the
complexity of developing
embedded software
Increase the quality of the
data you send to the cloud
through filtering device data
locally and only transmitting
the data you need so you
can achieve rich insight at
a lower cost
31. Where Do I Want To Process Data?
Infrastructure CloudPoPIoT Endpoint Gateway Appliance
Common Programming Model
Onboard
AWS
Cloud
Lambda
@Edge
Amazon
FreeRTOS
Greengrass
32. Features of Greengrass
Security
AWS-grade
security
Data and
state sync
Local
Device Shadows
Local
triggers
Local
Message Broker
Local
actions
Local
Lambda Functions
Machine
Inference
Local Execution
of ML Models
Protocol
Adapters
Local messaging
with other devices
Over the
Air Updates
Easily Update
Greengrass Core
Local
Resource Access
Lambdas interact
with peripherals
Amazon
FreeRTOS
Works together
out of the box
33. Benefits AWS Greengrass
Respond quickly
to local events
Operate
offline
Simplified device
programming
Reduce the cost of
IoT applications
AWS-grade
security
35. Images—Universal, Ubiquitous, and Essential
There are 3,700,000,000 internet users in 2017
1,200,000,000 photos will be taken in 2017 (9% YoY Growth)
Source: InfoTrends Worldwide
36. Amazon Rekognition
Extract rich metadata from visual content
Object and Scene
Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
Celebrity
Recognition
Image
Moderation
37. Why Use Rekognition?
Object & Scene Detection
• Photo-sharing apps can power smart searches and
quickly find cherished memories, such as weddings,
hiking, or sunsets
Facial Analysis
• Retail businesses can understand the demographics and
sentiment of in-store customers
Face Comparison
• Hotels & hospitality businesses can provide seamless
access for guests and VIPs
Facial Recognition
• Provide secondary authentication for existing applications
johnf
38. Object and scene detection makes it easy for you to add features
that search, filter, and curate large image libraries
DetectLabels
Flower
Arrangement
Chair
Coffee Table
Living Room Indoors
Furniture
Cushion
Vase
Maple
Villa
Plant
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard
Patio
Object & Scene Detection
Identify objects and scenes and provide confidence scores
39. Emotion Expressed
General Attributes
Facial Pose
Facial Landmarks
EyeLeft,EyeRight,Nose
RightPupil,LeftPupil
MouthRight,LeftEyeBrowUp
Bounding Box...
Happy
Surprised
Smile:True
EyesOpen:True
Beard:True
Mustache:True
Pitch
Roll
Yaw
Demographic Data
Age Range
Gender:Male
29–45
96.5%
Facial Analysis
Analyze facial characteristics in multiple dimensions
DetectFaces
Image Quality
Brightness
Sharpness
23.6%
99.9%
83.8%
0.65%
23.6%
99.8%
99.5%
99.9%
1.446
5.725
4.383
42. Detect explicit and suggestive contentRecognize thousands of famous individuals
DetectModerationLabelsRecognizeCelebrities
Celebrity Recognition & Image Moderation
Newly released Rekognition features
43. Interfacing with Rekognition
Optimizing your input & requests for best performance
• S3 input for API calls – max image size of 15MB
• 5MB limit for non-S3 (Base64 encoded) API calls
• Minimum image resolution (x or y) of 80 pixels
• Image data supported in PNG or JPG format
• Max number of faces in a single face collection is 1 million
• The max matching faces the search API returns is 4096
• Size of face should occupy 5%+ of image for detection
• Collections are for faces!
…
Use Amazon CloudWatch to observe & issue alerts on Rekognition metrics
45. Rekognition APIs—Overview
Rekognition’s computer vision API operations can be grouped into
Non-storage API operations, and Storage-based API operations
Non-storage API Operations Storage-based API Operations
{
"FaceMatches": [
{"Face": {"BoundingB
"Height":
0.2683333456516266,
"Left":
0.5099999904632568,
"Top":
0.1783333271741867,
"Width":
0.17888888716697693},
"
CompareFaces
DetectFaces
DetectLabels
DetectModerationLabels
GetCelebrityInfo
RecognizeCelebrities
{
"FaceMatches": [
{"Face": {"BoundingB
"Height":
0.2683333456516266,
"Left":
0.5099999904632568,
"Top":
0.1783333271741867,
"Width":
0.17888888716697693},
"
CreateCollection
DeleteCollection
DeleteFaces
IndexFaces
ListCollections
SearchFaces
SearchFacesByImage
ListFaces
46. What Can You Do with Amazon Rekognition?
Search for people, objects, scenes, and concepts across millions of images
Filter inappropriate or specific content
Redact identities from images of faces
Verify identities by matching against reference faces
Recognize individuals by matching faces to a collection
Analyze user traffic hotspots and journey paths by demographics and sentiment
47. Searchable Image Library
Real Estate Property Search
Property Search Amazon Elasticsearch
User captures an image
for their property listing
Mobile app uploads
the image to S3
A Lambda function is triggered
and calls Rekognition
Rekognition retrieves the image from S3 and
returns labels for the property and amenities
Lambda pushes the labels and
confidence scores to Elasticsearch
Other users can search properties
by landmarks, category, etc.
Photo Upload Amazon S3 AWS Lambda Detect Objects & Scenes
49. Face-Based User Verification
Confirm user identities by comparing their live image with a reference image
Authenticated User
Image Capture
Amazon S3
Compare Faces
Rekognition compares the live image
and the badge image—and returns
a similarity score
The application retrieves the
user’s badge from S3
Application
If the similarity score is over 92%,
the application returns a green status.
If not, an alert is issued to security staff
The application captures a live
image of each employee as they
scan their access card
50. Face-Based User Verification
Confirm user identities by comparing their live image with a reference image
• S3 Encryption of badge images—
SSE-S3, SSE-KMS, SSE-C
• Prevent tampering with bucket
policies & IAM RO permissions
• Extend by using Rek collections
• Cloudtrail—Logging & Auditing
with tamper-proof log signatures
• Tie notification into SNS/SES,
Custom CloudWatch Logs metrics,
or ElasticSearch with alerts
AWS
KMS
AWS
CloudTrail
AWS
Lambda
Amazon
S3
Amazon
SNS
AWS
CloudFormation
Amazon
CloudWatch
Amazon
SES
1
2
3
51. Facial Recognition
Identify individuals by matching a live image to a collection
of images of known persons
#0123 #0123
5426 128762
78426 45871
286546 26751
3861 945
Images SearchFacesByImage Face Collection
Person Details Table
Photo AppEnd User Amazon S3
Rekognition searches the face collection for
matches to the reference image and returns an
array of face metadata for potential face
matches, ordered by similarity
If source images are required,
they are retrieved from S3
The photo app displays search
results to the end user
52. Collections and Access Patterns
Logging—visitor logs, digital libraries
• Easily find specific images from a digital library
• Find certain images by using a reference image
Social Tagging—photo storage and sharing
• One collection per application user
• Automated friend tagging
Person Verification—employee gate check
• One collection for each person to be verified
• Detection of stolen/shared IDs
53. Rekognition APIs—Advanced Usage
Decision trees and processing pipelines
Why?
• Many use cases require more than a single operation
to arrive at actionable data
How?
• S3 event notifications, Lambda, Step Functions
• DynamoDB for persistent pipeline storage
• Augmenting results with 3rd Party AI/ML
• OpenCV, MXNet, etc. on EC2 Spot, ECS, AI/ML AMI
Sample Use Cases
• Person of interest near a celebrity
• Multi-pass motion detection enhancement
• Subjects leaving a location without possessions
IndexFaces
DetectLabels
“person”
55. Automating Footage Tagging with
Amazon Rekognition
• Built in three weeks
• Indexed against 99,000 people
• Index created in one day
• Saved ~9,000 hours a year in
manual curation costs
• Live video with frame sampling
Previously, only about half of all footage
was indexed due to the immense time
requirements required by manual processes
57. Visual Search
Open Influence is a market leader in the
influencer marketing space and enables
global brands and agencies to identify
relevant influencers
• Real-time visual search, powered by
Rekognition, enables Open Influence to
tag millions of social images accurately
• Using Rekognition allowed Open Influence to
cut down the time it takes to source relevant
influencers from 2–3 days to minutes
58. Metadata Tagging
Scripps Networks Interactive is a leading
developer of engaging lifestyle content
• Instead of manually tagging media
assets, Rekognition enables Scripps
Networks Interactive to save time and
increase productivity with automated
metadata tagging