Serverless Industry 4.0 & AI: Drive Business Insights from Machine Data (IOT402-R1) - AWS re:Invent 2018
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In this workshop, we provide a hands-on approach for building the complete Industry 4.0 AI dataflow – from accessing data on machines to deploying AI models on the edge. Learn how to combine fully managed AWS IoT services for such a solution. First, we connect different industrial devices using AWS Greengrass with industry protocols (e.g., OPC UA). Next, we use AWS IoT Analytics to create AI models with Amazon SageMaker. Finally, we deploy and run models with AWS Greengrass Machine Learning (ML) Inference.
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Lower Costs on Amazon EMR: Auto Scaling, Spot Pricing, & Expert Strategies (A...
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Optimize Your Oracle Licenses on Amazon Web Services (DAT211) - AWS re:Invent...
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Scale up a Web Application (ARC409-R) - AWS re:Invent 2018
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AWS DevOps Essentials: An Introductory Workshop on CI/CD Best Practices (DEV3...
AWS DevOps Essentials: An Introductory Workshop on CI/CD Best Practices (DEV3...
Modernizing .NET Applications on AWS (GPSCT204) - AWS re:Invent 2018
Modernizing .NET Applications on AWS (GPSCT204) - AWS re:Invent 2018
From Monolith to Modern Apps: Best Practices (SRV322-R2) - AWS re:Invent 2018
From Monolith to Modern Apps: Best Practices (SRV322-R2) - AWS re:Invent 2018
Running Enterprise Test/Dev on Amazon EC2 Spot Instances (CMP407-R1) - AWS re...
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Run Your CI/CD and Test Workloads for 90% Less with Amazon EC2 Spot Instances...
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Secure Your Site: Use CDN Security Features to Protect Your Content & Infrast...
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Real-World Use Cases for Amazon DynamoDB (DAT333) - AWS re:Invent 2018
Real-World Use Cases for Amazon DynamoDB (DAT333) - AWS re:Invent 2018
One-Click Infrastructure for Your Alexa Skill (GPSCT407) - AWS re:Invent 2018
One-Click Infrastructure for Your Alexa Skill (GPSCT407) - AWS re:Invent 2018
SaaS Analytics and Metrics: Capturing and Surfacing the Data That's Fundament...
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Design, Deploy, and Optimize Microsoft SQL Server on AWS (WIN324-R1) - AWS re...
Design, Deploy, and Optimize Microsoft SQL Server on AWS (WIN324-R1) - AWS re...
Similar to Serverless Industry 4.0 & AI: Drive Business Insights from Machine Data (IOT402-R1) - AWS re:Invent 2018
AWS Snowball Edge and AWS Greengrass for Fun and Profit (STG388) - AWS re:Inv...
This document discusses AWS Snowball Edge and AWS Greengrass. It provides an overview of the key features of AWS Snowball Edge, including its S3-compatible storage, compute capabilities, and ability to cluster multiple devices. It also discusses how AWS Greengrass can help bring cloud benefits to IoT edge devices by allowing them to run Lambda functions locally for tasks like data preprocessing. Several use cases for edge computing with these services are described, such as preprocessing sensor data from vehicles before uploading to the cloud.
Industrial IoT Applications: Making the Connection and Extracting Value (IOT3...
Industrial IoT applications are rapidly emerging across industries such as oil and gas, manufacturing, and agriculture. In this chalk talk, we help you architect end-to-end solutions that will deliver value like predictive maintenance, manufacturing quality, and process monitoring. In this interactive session, we help you understand how to connect greenfield and brownfield infrastructure with AWS that leverages both AWS Greengrass (on premises) and other AWS Cloud services. Along the way, we show how the AWS Industrial IoT Reference Architecture is incorporated to build your industrial application.
Enable Your Smart Factory with the AWS Industrial IoT Reference Solution (MFG...
The document discusses enabling a smart factory with AWS Industrial IoT Reference Solution. It covers why smart factories are useful by utilizing untapped data for business insights. The document then discusses industrial communication protocols and manufacturing networking. It presents the AWS industrial IoT reference architecture and shows a hands-on workshop that will be conducted to set up a simulated manufacturing network on AWS with PLC simulation and data collection in IoT.
Using AWS IoT for Industrial Applications - AWS Online Tech Talks
Learning Objectives:
- Understand how AWS IoT can be used for Industrial Applications including predictive quality, asset condition monitoring, and predictive maintenance
- Know how features of AWS IoT Core such as the rules engine, device shadow, and message broker are used for industrial applications
- Articulate how AWS IoT Analytics supports machine learning
Edge Computing with AWS Greengrass
In this workshop, learn how to connect devices to AWS IoT and AWS Greengrass. Understand the architecture, and install and configure device communication using AWS Greengrass. In addition, take advantage of the opportunity to create various device communication scenarios with AWS Greengrass and simulate the data flow with sensor data. Attendees in workshop need an AWS account and are asked to bring their laptop.
Machine learning in the physical world by Kip Larson from AWS IoT
Presented at AI NEXTCon Seattle 1/17-20, 2018
http://aisea18.xnextcon.com
join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..
Visually developing IoT applications using AWS IoT Things Graph - SVC207 - Ch...
AWS IoT Things Graph makes it easy to visually connect different devices and web services to build Internet of Things (IoT) applications, with little to no code. In this session, we show you how to quickly design, deploy, and monitor sophisticated workflows. AWS IoT Things Graph allows you to represent your devices and web services as reusable components called models, which hide low-level details like data formats, protocols, and interfaces. We explore how you then can visually “wire together” your models and their interactions to rapidly design and deploy complex custom IoT applications.
Deep Dive into New AWS IoT Services Launched in 2018 (IOT320) - AWS re:Invent...
Take a deep dive into the newer services available in 2018 for AWS IoT. In this session, we look at new features added to AWS IoT Core and AWS Greengrass with Edge ML, and then deep dive into AWS IoT Analytics, AWS Device Management, and AWS IoT Device Defender. Learn more about when to use some of these services and explore comparisons between them.
Integrate the AWS Cloud with Responsive Xilinx Machine Learning at the Edge (...
In this workshop, learn how you can integrate Xilinx edge machine learning with massive scale AWS Cloud analytics, machine learning model building, and dashboards. Based on an Oil & Gas scenario, you will learn how to combine AWS Cloud services with AWS Greengrass on Zynq Ultrascale+ and Amazon FreeRTOS on Xilinx Zynq-7000. After this workshop, you will have a concrete understanding of Machine Learning applicability at the edge and its relationship with the AWS Cloud.
Aws Tools for Alexa Skills
Alexa skills allow you to expand the voice assistant capabilities beyond what comes out of the box. In addition to more than 30,000 available Alexa skills out there, you can start developing your own skill tomorrow morning.
This session will give you an high level review of the different AWS services that can help you develop and run your skill in minutes. We will cover AWS Lambda, DynamoDB, S3 and other tools and services that will help you run your skills at scale.
[NEW LAUNCH!] Introducing AWS IoT Things Graph (IOT366) - AWS re:Invent 2018
The document discusses AWS IoT Things Graph, a visual tool for building automation applications using devices connected to AWS IoT. It describes how Things Graph allows non-programmers to visually create workflows by connecting device models and defining interactions without code. It also discusses how Things Graph applications can be deployed to AWS Greengrass devices and monitored using CloudWatch metrics. Two examples of using Things Graph for smart home applications are provided.
Tips for Building IoT Applications Faster (IOT366) - AWS re:Invent 2018
AWS IoT services help you connect devices to AWS services and other devices, secure data, interactions, and process, act on device data, and enable applications to interact with devices, even when they are offline. In this session, we provide tips for using the portfolio to create applications faster and deploying applications at the edge. We show you how to save time when adding new kinds of devices to your applications.
Machine Learning at the IoT Edge (IOT214) - AWS re:Invent 2018
Whether it's connected cars, smart home devices, or industrial applications, IoT applications are rapidly becoming more intelligent. Edge computing is helping lead this transformation as IoT devices not only collect and transmit data, but also perform predictive analytics and respond to local events, even without cloud connectivity. In this session, learn about ML inference at the edge, why it matters, and how to use it to build intelligent IoT applications. Through customer use cases, we demonstrate how to use AWS Greengrass to locate cloud-trained ML models, deploy them to your AWS Greengrass devices, enable access to on-device computing power, and apply the models to locally generated data without connection to the cloud.
Breaking down an Industrial IoT reference architecture.pptx
The document presents an industrial IoT reference architecture for connecting industrial facilities to the cloud. It includes:
- A breakdown of the common business and technical challenges faced in industrial digital transformation.
- A low-level reference architecture showing how to create a unified OT/IT data backbone integrating edge, cloud, security, data management, applications and insights.
- Examples of how the architecture enables use cases across industries like predictive maintenance, quality control and remote monitoring.
- Recommendations around executing a modern data strategy and taking a holistic approach to maximize insights, minimize technical debt and future-proof the solution.
The Intelligent Edge for IoT: Help Customers Harness the Power of Connected I...
At AWS, we bring together our partners and AWS IoT services to offer solutions, including hardware solutions, that leverage edge and cloud technologies used to build IoT applications with edge computing capabilities. These solutions provide customers with the intelligence needed to achieve real business outcomes. In this session, learn how we work with our partner community to develop strategies and build solutions.
How AWS can help you on quality and data products
This document discusses how AWS can help with quality and data products. It provides an overview of AWS capabilities for data analytics including machine learning, artificial intelligence, and big data. It also discusses how these capabilities can help industries like manufacturing through use cases like predictive maintenance, production quality monitoring, and real-time process optimization. Finally, it presents a case study of a discrete manufacturing company using IoT and edge computing on AWS to connect machines and sensors for issues detection and root cause analysis.
IoT at the Edge: Using AR & ML to Provide Real-Time Industrial Monitoring (AR...
In this session, we showcase how augmented reality (AR) can be used to create a "digital twin" for use in real-time, in-field monitoring of equipment and machines. We demonstrate a hands-on experience and showcase the reference architecture of integrating AWS IoT, ML, and Amazon Sumerian. For enterprise customers, we also show the benefits of integrating AWS Amplify for security and privacy.
Building IoT Applications for a Smart Home, ft. Vestel (IOT306-R1) - AWS re:I...
The IoT transformation begins at home. Today, device manufacturers and network providers are building groundbreaking connected devices and applications to make your home smarter. In this session, we dive into the best practices and common patterns for building connected devices and applications for a Smart Home. This session focuses on securely onboarding your devices to a consumer's account, leveraging IoT, using ML at the edge, and video streaming for home security. By the end of the session, you'll have a set of best practices for how to build IoT products in the Smart Home. Also hear from Vestel about their smart home application.
Distributed Solar Systems at EDF Renewables and AWS IoT: A Natural Fit (PUT30...
The AWS suite of managed services for IoT enables companies to quickly and easily deploy devices to the edge and synchronize their industrial time-series data from multiple sites to the AWS Cloud, where advanced analytics and machine learning can generate valuable insights about their business. In this session, learn how EDF Renewables used AWS Greengrass, AWS IoT Core, AWS IoT Analytics, and AWS Lambda to facilitate the collection, aggregation, and quality assurance of operational data from solar installations. Hear how working with AWS Professional Services transformed its approach to product development, and learn what challenges and solutions came with choosing leading-edge services form AWS.
IIoT_ML_Architechure_AWS
This document discusses architectures for industrial IoT and machine learning platforms using AWS services. It covers key considerations for domains like control, operations, and information. Example use cases include predictive quality, asset condition monitoring, and predictive maintenance using industrial data. The document outlines how to collect machine data from sources like sensors using protocols and technologies like MQTT, OPC-UA, and AWS IoT SiteWise. It proposes architectures with edge gateways, industrial data centers, and streaming the data to AWS services for storage, processing, and machine learning including AWS Kinesis, Kafka, S3, Glue, Athena, SageMaker and QuickSight.
Similar to Serverless Industry 4.0 & AI: Drive Business Insights from Machine Data (IOT402-R1) - AWS re:Invent 2018 (20)
AWS Snowball Edge and AWS Greengrass for Fun and Profit (STG388) - AWS re:Inv...
AWS Snowball Edge and AWS Greengrass for Fun and Profit (STG388) - AWS re:Inv...
Industrial IoT Applications: Making the Connection and Extracting Value (IOT3...
Industrial IoT Applications: Making the Connection and Extracting Value (IOT3...
Enable Your Smart Factory with the AWS Industrial IoT Reference Solution (MFG...
Enable Your Smart Factory with the AWS Industrial IoT Reference Solution (MFG...
Using AWS IoT for Industrial Applications - AWS Online Tech Talks
Using AWS IoT for Industrial Applications - AWS Online Tech Talks
Machine learning in the physical world by Kip Larson from AWS IoT
Machine learning in the physical world by Kip Larson from AWS IoT
Visually developing IoT applications using AWS IoT Things Graph - SVC207 - Ch...
Visually developing IoT applications using AWS IoT Things Graph - SVC207 - Ch...
Deep Dive into New AWS IoT Services Launched in 2018 (IOT320) - AWS re:Invent...
Deep Dive into New AWS IoT Services Launched in 2018 (IOT320) - AWS re:Invent...
Integrate the AWS Cloud with Responsive Xilinx Machine Learning at the Edge (...
Integrate the AWS Cloud with Responsive Xilinx Machine Learning at the Edge (...
[NEW LAUNCH!] Introducing AWS IoT Things Graph (IOT366) - AWS re:Invent 2018
[NEW LAUNCH!] Introducing AWS IoT Things Graph (IOT366) - AWS re:Invent 2018
Tips for Building IoT Applications Faster (IOT366) - AWS re:Invent 2018
Tips for Building IoT Applications Faster (IOT366) - AWS re:Invent 2018
Machine Learning at the IoT Edge (IOT214) - AWS re:Invent 2018
Machine Learning at the IoT Edge (IOT214) - AWS re:Invent 2018
Breaking down an Industrial IoT reference architecture.pptx
Breaking down an Industrial IoT reference architecture.pptx
The Intelligent Edge for IoT: Help Customers Harness the Power of Connected I...
The Intelligent Edge for IoT: Help Customers Harness the Power of Connected I...
IoT at the Edge: Using AR & ML to Provide Real-Time Industrial Monitoring (AR...
IoT at the Edge: Using AR & ML to Provide Real-Time Industrial Monitoring (AR...
Building IoT Applications for a Smart Home, ft. Vestel (IOT306-R1) - AWS re:I...
Building IoT Applications for a Smart Home, ft. Vestel (IOT306-R1) - AWS re:I...
Distributed Solar Systems at EDF Renewables and AWS IoT: A Natural Fit (PUT30...
Distributed Solar Systems at EDF Renewables and AWS IoT: A Natural Fit (PUT30...
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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.
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Open banking as a service
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
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• Monetizing data / APIs
• Business models
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Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
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Serverless Industry 4.0 & AI: Drive Business Insights from Machine Data (IOT402-R1) - AWS re:Invent 2018
- 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Serverless Industry 4.0 & AI: Drive Business Insights from Machine Data I O T 4 0 2
- 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Slide 3 What the workshop covers • Data from industrial machines (drilling machine) • Local industrial connectivity with AWS Greengrass • Forward data to the cloud (AWS IoT Core) • Store and analyze the data (AWS IoT Analytics with Amazon SageMaker) • Build a ML model (Amazon SageMaker notebook) • Deploy ML model to AWS Greengrass together with an inference AWS Lambda function • Do local prediction and feedback to industrial devices (tower light)
- 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Slide 4 Bosch Rexroth Demonstrator • Sample data? • Can we provoke anomalies? • AWS Greengrass on the gateway?
- 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Slide 5 Participant virtual operations (OT) – Separate AWS AccountOn-stage operations (OT) Workshop architecture AWS IoT Core AWS Greengrass Signal tower Amazon S3 ML model ML model Sensor data Sensor data Virtual drill EC2 Virtual gateway EC2 Web-Dashboard (Port 80)AWS Greengrass AWS IoT Core Amazon S3 ML model ML model Sensor data Sensor data Cross-Account sensor data
- 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 7. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.