Tag Archives: IOT

AWS Architecture Monthly Magazine: Manufacturing

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/aws-architecture-monthly-magazine-manufacturing/

Architecture Monthly Magazine - Nov-Dec 2019

For more than 25 years, Amazon has designed and manufactured smart products and distributed billions of products through its globally connected distribution network using cutting edge automation, machine learning and AI, and robotics, with AWS at its core. From product design to smart factory and smart products, AWS helps leading manufacturers transform their manufacturing operations with the most comprehensive and advanced set of cloud solutions available today, while taking advantage of the highest level of security.

In this Manufacturing-themed end-of-year issue of the AWS Architecture Monthly magazine, Steve Blackwell, AWS Manufacturing Tech Leader, talks about how manufacturers can experiment with and take advantage of emerging technologies using three main architectural patterns: demand forecasting, smart factories, and extending the manufacturing value chain with smart products.

In This Issue

We’ve assembled architectural best practices about Manufacturing from all over AWS, and we’ve made sure that a broad audience can appreciate it. Note that this will be our last issue of the year. We’ll be back in January with highlights and insights about AWS re:Invent 2019 (December 2-6 in Las Vegas).

  • Case Study: iRobot Ready to Unlock the Next Generation of Smart Homes Using the AWS Cloud
  • Ask an Expert: Steve Blackwell, Manufacturing Tech Leader
  • Blog Post: Reinventing the IoT Platform for Discrete Manufacturers
  • Solution: Smart Product Solution
  • AWS Coffee Break: IoT Helps Manufacturing Hit the Right Note
  • Whitepaper: Practical Ways To Achieve Smarter, Faster, and More Responsive Operations
  • Reference Architecture: EDA on AWS with IBM Spectrum LSF

How to Access the Magazine

We hope you’re enjoying Architecture Monthly, and we’d like to hear from you—leave us star rating and comment on the Amazon Kindle Newsstand page or contact us anytime at [email protected].

FogHorn: Edge-to-Edge Communication and Deep Learning

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/foghorn-edge-to-edge-communication-and-deep-learning/

FogHorn is an intelligent Internet of Things ( IoT) edge solution that delivers data processing and real-time inference where data is created. Referring to itself as “the only ‘real’ edge intelligence solution in the market today,”  FogHorn is powered by a hyper-efficient Complex Event Processor (CEP) and delivers comprehensive data enrichment and real-time analytics on high volumes, varieties, and velocities of streaming sensor data, and is optimized for constrained compute footprints and limited connectivity.

Andrea Sabet, AWS Solutions Architect speaks with Ramya Ravichandar, Vice President of Products at Foghorn to talk about how FogHorn integrates with IoT MQTT for edge-to-edge communication as well as Amazon SageMaker for deep learning model deployment. The edgefication process involves running inference with real-time streaming data against a trained deep learning model. Drifts in the model accuracy trigger a callback to SageMaker for retraining.

*Check out more This Is My Architecture video series.

 

Visualizing Sensor Data in Amazon QuickSight

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/visualizing-sensor-data-in-amazon-quicksight/

This post is courtesy of Moheeb Zara, Developer Advocate, AWS Serverless

The Internet of Things (IoT) is a term used wherever physical devices are networked in some meaningful connected way. Often, this takes the form of sensor data collection and analysis. As the number of devices and size of data scales, it can become costly and difficult to keep up with demand.

Using AWS Serverless Application Model (AWS SAM), you can reduce the cost and time to market of an IoT solution. This guide demonstrates how to collect and visualize data from a low-cost, Wi-Fi connected IoT device using a variety of AWS services. Much of this can be accomplished within the AWS Free Usage Tier, which is necessary for the following instructions.

Services used

The following services are used in this example:

What’s covered in this post?

This post covers:

  • Connecting an Arduino MKR 1010 Wi-Fi device to AWS IoT Core.
  • Forwarding messages from an AWS IoT Core topic stream to a Lambda function.
  • Using a Kinesis Data Firehose delivery stream to store data in S3.
  • Analyzing and visualizing data stored in S3 using Amazon QuickSight.

Connect the device to AWS IoT Core using MQTT

The Arduino MKR 1010 is a low-cost, Wi-Fi enabled, IoT device, shown in the following image.

An Arduino MKR 1010 Wi-Fi microcontroller

Its analog and digital input and output pins can be used to read sensors or to write to actuators. Arduino provides a detailed guide on how to securely connect this device to AWS IoT Core. The following steps build upon it to push arbitrary sensor data to a topic stream and ultimately visualize that data using Amazon QuickSight.

  1. Start by following this comprehensive guide to using an Arduino MKR 1010 with AWS IoT Core. Upon completion, your device is connected to AWS IoT Core using MQTT (Message Queuing Telemetry Transport), a protocol for publishing and subscribing to messages using topics.
  2. In the Arduino IDE, choose File, Sketch, Include Library, and Manage Libraries.
  3. In the window that opens, search for ArduinoJson and select the library by Benoit Blanchon. Choose install.

4. Add #include <ArduinoJson.h> to the top of your sketch from the Arduino guide.

5. Modify the publishMessage() function with this code. It publishes a JSON message with two keys: time (ms) and the current value read from the first analog pin.

void publishMessage() {  
  Serial.println("Publishing message");

  // send message, the Print interface can be used to set the message contents
  mqttClient.beginMessage("arduino/outgoing");
  
  // create json message to send
  StaticJsonDocument<200> doc;
  doc["time"] = millis();
  doc["sensor_a0"] = analogRead(0);
  serializeJson(doc, mqttClient); // print to client
  
  mqttClient.endMessage();
}

6. Save and upload the sketch to your board.

Create a Kinesis Firehose delivery stream

Amazon Kinesis Data Firehose is a service that reliably loads streaming data into data stores, data lakes, and analytics tools. Amazon QuickSight requires a data store to create visualizations of the sensor data. This simple Kinesis Data Firehose delivery stream continuously uploads data to an S3 storage bucket. The next sections cover how to add records to this stream using a Lambda function.

  1. In the Kinesis Data Firehose console, create a new delivery stream, called SensorDataStream.
  2. Leave the default source as a Direct PUT or other sources and choose Next.
  3. On the next screen, leave all the default values and choose Next.
  4. Select Amazon S3 as the destination and create a new bucket with a unique name. This is where records are continuously uploaded so that they can be used by Amazon QuickSight.
  5. On the next screen, choose Create New IAM Role, Allow. This gives the Firehose delivery stream permission to upload to S3.
  6. Review and then choose Create Delivery Stream.

It can take some time to fully create the stream. In the meantime, continue on to the next section.

Invoking Lambda using AWS IoT Core rules

Using AWS IoT Core rules, you can forward messages from devices to a Lambda function, which can perform actions such as uploading to an Amazon DynamoDB table or an S3 bucket, or running data against various Amazon Machine Learning services. In this case, the function transforms and adds a message to the Kinesis Data Firehose delivery stream, which then adds that data to S3.

AWS IoT Core rules use the MQTT topic stream to trigger interactions with other AWS services. An AWS IoT Core rule is created by using an SQL statement, a topic filter, and a rule action. The Arduino example publishes messages every five seconds on the topic arduino/outgoing. The following instructions show how to consume those messages with a Lambda function.

Create a Lambda function

Before creating an AWS IoT Core rule, you need a Lambda function to consume forwarded messages.

  1. In the AWS Lambda console, choose Create function.
  2. Name the function ArduinoConsumeMessage.
  3. For Runtime, choose Author From Scratch, Node.js10.x. For Execution role, choose Create a new role with basic Lambda permissions. Choose Create.
  4. On the Execution role card, choose View the ArduinoConsumeMessage-role-xxxx on the IAM console.
  5. Choose Attach Policies. Then, search for and select AmazonKinesisFirehoseFullAccess.
  6. Choose Attach Policy. This applies the necessary permissions to add records to the Firehose delivery stream.
  7. In the Lambda console, in the Designer card, select the function name.
  8. Paste the following in the code editor, replacing SensorDataStream with the name of your own Firehose delivery stream. Choose Save.
const AWS = require('aws-sdk')

const firehose = new AWS.Firehose()
const StreamName = "SensorDataStream"

exports.handler = async (event) => {
    
    console.log('Received IoT event:', JSON.stringify(event, null, 2))
    
    let payload = {
        time: new Date(event.time),
        sensor_value: event.sensor_a0
    }
    
    let params = {
            DeliveryStreamName: StreamName,
            Record: { 
                Data: JSON.stringify(payload)
            }
        }
        
    return await firehose.putRecord(params).promise()

}

Create an AWS IoT Core rule

To create an AWS IoT Core rule, follow these steps.

  1. In the AWS IoT console, choose Act.
  2. Choose Create.
  3. For Rule query statement, copy and paste SELECT * FROM 'arduino/outgoing’. This subscribes to the outgoing message topic used in the Arduino example.
  4. Choose Add action, Send a message to a Lambda function, Configure action.
  5. Select the function created in the last set of instructions.
  6. Choose Create rule.

At this stage, any message published to the arduino/outgoing topic forwards to the ArduinoConsumeMessage Lambda function, which transforms and puts the payload on the Kinesis Data Firehose stream and also logs the message to Amazon CloudWatch. If you’ve connected an Arduino device to AWS IoT Core, it publishes to that topic every five seconds.

The following steps show how to test functionality using the AWS IoT console.

  1. In the AWS IoT console, choose Test.
  2. For Publish, enter the topic arduino/outgoing .
  3. Enter the following test payload:
    {
      “time”: 1567023375013,  
      “sensor_a0”: 456
    }
  4. Choose Publish to topic.
  5. Navigate back to your Lambda function.
  6. Choose Monitoring, View logs in CloudWatch.
  7. Select a log item to view the message contents, as shown in the following screenshot.

Visualizing data with Amazon QuickSight

To visualize data with Amazon QuickSight, follow these steps.

  1. In the Amazon QuickSight console, sign up.
  2. Choose Manage Data, New Data Set. Select S3 as the data source.
  3. A manifest file is necessary for Amazon QuickSight to be able to fetch data from your S3 bucket. Copy the following into a file named manifest.json. Replace YOUR-BUCKET-NAME with the name of the bucket created for the Firehose delivery stream.
    {
       "fileLocations":[
          {
             "URIPrefixes":[
                "s3://YOUR-BUCKET-NAME/"
             ]
          }
       ],
       "globalUploadSettings":{
          "format":"JSON"
       }
    }
  4. Upload the manifest.json file.
  5. Choose Connect, then Visualize. You may have to give Amazon QuickSight explicit permissions to your S3 bucket.
  6. Finally, design the Amazon QuickSight visualizations in the drag and drop editor. Drag the two available fields into the center card to generate a Sum of Sensor_value by Time visual.

Conclusion

This post demonstrated visualizing data from a securely connected remote IoT device. This was achieved by connecting an Arduino to AWS IoT Core using MQTT, forwarding messages from the topic stream to Lambda using IoT Core rules, putting records on an Amazon Kinesis Data Firehose delivery stream, and using Amazon QuickSight to visualize the data stored within an S3 bucket.

With these building blocks, it is possible to implement highly scalable and customizable IoT data collection, analysis, and visualization. With the use of other AWS services, you can build a full end-to-end platform for an IoT product that can reliably handle volume. To further explore how hardware and AWS Serverless can work together, visit the Amazon Web Services page on Hackster.

Learn about AWS Services & Solutions – September AWS Online Tech Talks

Post Syndicated from Jenny Hang original https://aws.amazon.com/blogs/aws/learn-about-aws-services-solutions-september-aws-online-tech-talks/

Learn about AWS Services & Solutions – September AWS Online Tech Talks

AWS Tech Talks

Join us this September to learn about AWS services and solutions. The AWS Online Tech Talks are live, online presentations that cover a broad range of topics at varying technical levels. These tech talks, led by AWS solutions architects and engineers, feature technical deep dives, live demonstrations, customer examples, and Q&A with AWS experts. Register Now!

Note – All sessions are free and in Pacific Time.

Tech talks this month:

 

Compute:

September 23, 2019 | 11:00 AM – 12:00 PM PTBuild Your Hybrid Cloud Architecture with AWS – Learn about the extensive range of services AWS offers to help you build a hybrid cloud architecture best suited for your use case.

September 26, 2019 | 1:00 PM – 2:00 PM PTSelf-Hosted WordPress: It’s Easier Than You Think – Learn how you can easily build a fault-tolerant WordPress site using Amazon Lightsail.

October 3, 2019 | 11:00 AM – 12:00 PM PTLower Costs by Right Sizing Your Instance with Amazon EC2 T3 General Purpose Burstable Instances – Get an overview of T3 instances, understand what workloads are ideal for them, and understand how the T3 credit system works so that you can lower your EC2 instance costs today.

 

Containers:

September 26, 2019 | 11:00 AM – 12:00 PM PTDevelop a Web App Using Amazon ECS and AWS Cloud Development Kit (CDK) – Learn how to build your first app using CDK and AWS container services.

 

Data Lakes & Analytics:

September 26, 2019 | 9:00 AM – 10:00 AM PTBest Practices for Provisioning Amazon MSK Clusters and Using Popular Apache Kafka-Compatible Tooling – Learn best practices on running Apache Kafka production workloads at a lower cost on Amazon MSK.

 

Databases:

September 25, 2019 | 1:00 PM – 2:00 PM PTWhat’s New in Amazon DocumentDB (with MongoDB compatibility) – Learn what’s new in Amazon DocumentDB, a fully managed MongoDB compatible database service designed from the ground up to be fast, scalable, and highly available.

October 3, 2019 | 9:00 AM – 10:00 AM PTBest Practices for Enterprise-Class Security, High-Availability, and Scalability with Amazon ElastiCache – Learn about new enterprise-friendly Amazon ElastiCache enhancements like customer managed key and online scaling up or down to make your critical workloads more secure, scalable and available.

 

DevOps:

October 1, 2019 | 9:00 AM – 10:00 AM PT – CI/CD for Containers: A Way Forward for Your DevOps Pipeline – Learn how to build CI/CD pipelines using AWS services to get the most out of the agility afforded by containers.

 

Enterprise & Hybrid:

September 24, 2019 | 1:00 PM – 2:30 PM PT Virtual Workshop: How to Monitor and Manage Your AWS Costs – Learn how to visualize and manage your AWS cost and usage in this virtual hands-on workshop.

October 2, 2019 | 1:00 PM – 2:00 PM PT – Accelerate Cloud Adoption and Reduce Operational Risk with AWS Managed Services – Learn how AMS accelerates your migration to AWS, reduces your operating costs, improves security and compliance, and enables you to focus on your differentiating business priorities.

 

IoT:

September 25, 2019 | 9:00 AM – 10:00 AM PTComplex Monitoring for Industrial with AWS IoT Data Services – Learn how to solve your complex event monitoring challenges with AWS IoT Data Services.

 

Machine Learning:

September 23, 2019 | 9:00 AM – 10:00 AM PTTraining Machine Learning Models Faster – Learn how to train machine learning models quickly and with a single click using Amazon SageMaker.

September 30, 2019 | 11:00 AM – 12:00 PM PTUsing Containers for Deep Learning Workflows – Learn how containers can help address challenges in deploying deep learning environments.

October 3, 2019 | 1:00 PM – 2:30 PM PTVirtual Workshop: Getting Hands-On with Machine Learning and Ready to Race in the AWS DeepRacer League – Join DeClercq Wentzel, Senior Product Manager for AWS DeepRacer, for a presentation on the basics of machine learning and how to build a reinforcement learning model that you can use to join the AWS DeepRacer League.

 

AWS Marketplace:

September 30, 2019 | 9:00 AM – 10:00 AM PTAdvancing Software Procurement in a Containerized World – Learn how to deploy applications faster with third-party container products.

 

Migration:

September 24, 2019 | 11:00 AM – 12:00 PM PTApplication Migrations Using AWS Server Migration Service (SMS) – Learn how to use AWS Server Migration Service (SMS) for automating application migration and scheduling continuous replication, from your on-premises data centers or Microsoft Azure to AWS.

 

Networking & Content Delivery:

September 25, 2019 | 11:00 AM – 12:00 PM PTBuilding Highly Available and Performant Applications using AWS Global Accelerator – Learn how to build highly available and performant architectures for your applications with AWS Global Accelerator, now with source IP preservation.

September 30, 2019 | 1:00 PM – 2:00 PM PTAWS Office Hours: Amazon CloudFront – Just getting started with Amazon CloudFront and [email protected]? Get answers directly from our experts during AWS Office Hours.

 

Robotics:

October 1, 2019 | 11:00 AM – 12:00 PM PTRobots and STEM: AWS RoboMaker and AWS Educate Unite! – Come join members of the AWS RoboMaker and AWS Educate teams as we provide an overview of our education initiatives and walk you through the newly launched RoboMaker Badge.

 

Security, Identity & Compliance:

October 1, 2019 | 1:00 PM – 2:00 PM PTDeep Dive on Running Active Directory on AWS – Learn how to deploy Active Directory on AWS and start migrating your windows workloads.

 

Serverless:

October 2, 2019 | 9:00 AM – 10:00 AM PTDeep Dive on Amazon EventBridge – Learn how to optimize event-driven applications, and use rules and policies to route, transform, and control access to these events that react to data from SaaS apps.

 

Storage:

September 24, 2019 | 9:00 AM – 10:00 AM PTOptimize Your Amazon S3 Data Lake with S3 Storage Classes and Management Tools – Learn how to use the Amazon S3 Storage Classes and management tools to better manage your data lake at scale and to optimize storage costs and resources.

October 2, 2019 | 11:00 AM – 12:00 PM PTThe Great Migration to Cloud Storage: Choosing the Right Storage Solution for Your Workload – Learn more about AWS storage services and identify which service is the right fit for your business.

 

 

The world’s first Raspberry Pi-powered Twitter-activated jelly bean-pooping unicorn

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/raspberry-pi-powered-twitter-activated-jelly-bean-pooping-unicorn/

When eight-year-old Tru challenged the Kids Invent Stuff team to build a sparkly, pooping, rainbow unicorn electric vehicle, they did exactly that. And when Kids Invent Stuff, also known as Ruth and Shawn, got in contact with Estefannie Explains it All, their unicorn ended up getting an IoT upgrade…because obviously.

You tweet and the Unicorn poops candy! | Kids Invent Stuff

We bring kids’ inventions to life and this month we teamed up with fellow youtube Estefannie (from Estefannie Explains It All https://www.youtube.com/user/estefanniegg SHE IS EPIC!) to modify Tru’s incredible sweet pooping unicorn to be activated by the internet! Featuring the AMAZING Allen Pan https://www.youtube.com/channel/UCVS89U86PwqzNkK2qYNbk5A (Thanks Allen for your filming and tweeting!)

Kids Invent Stuff

If you’re looking for an exciting, wholesome, wonderful YouTube channel suitable for the whole family, look no further than Kids Invent Stuff. Challenging kids to imagine wonderful inventions based on monthly themes, channel owners Ruth and Shawn then make these kids’ ideas a reality. Much like the Astro Pi Challenge, Kids Invent Stuff is one of those things we adults wish existed when we were kids. We’re not jealous, we’re just…OK, we’re definitely jealous.

ANYWAY, when eight-year-old Tru’s sparkly, pooping, rainbow unicorn won the channel’s ‘crazy new vehicle’ challenge, the team got to work, and the result is magical.

Riding an ELECTRIC POOPING UNICORN! | Kids Invent Stuff

We built 8-year-old Tru’s sparkly, pooping, rainbow unicorn electric vehicle and here’s what happened when we drove it for the first time and pooped out some jelly beans! A massive THANK YOU to our challenge sponsor The Big Bang Fair: https://www.thebigbangfair.co.uk The Big Bang Fair is the UK’s biggest celebration of STEM for young people!

But could a sparkly, pooping, rainbow unicorn electric vehicle ever be enough? Is anything ever enough if it’s not connected to the internet? Of course not. And that’s where Estefannie came in.

At Maker Central in Birmingham earlier this year, the two YouTube teams got together to realise their shared IoT dream.

They ran out of chairs for their panel, so Allen had to improvise

With the help of a Raspberry Pi Zero W connected to the relay built into the unicorn, the team were able to write code that combs through Twitter, looking for mentions of @mythicalpoops. A positive result triggers the Raspberry Pi to activate the relay, and the unicorn lifts its tail to shoot jelly beans at passers-by.

You can definitely tell this project was invented by an eight-year-old, and we love it!

IoT unicorn

As you can see in the video above, the IoT upgrades to the unicorn allow Twitter users to control when the mythical beast poops its jelly beans. There are rumours that the unicorn may be coming to live with us at Pi Towers, but if these turn out to be true, we’ll ensure that this function is turned off. So no tweeting the unicorn!

You know what to do

Be sure to subscribe to both Kids Invent Stuff and Estefannie Explains It All on YouTube. They’re excellent makers producing wonderful content, and we know you’ll love them.

How to milk a unicorn

The post The world’s first Raspberry Pi-powered Twitter-activated jelly bean-pooping unicorn appeared first on Raspberry Pi.

IoT community sprinkler system using Raspberry Pi | The MagPi issue 83

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/iot-community-sprinkler-system-using-raspberry-pi-the-magpi-issue-83/

Saving water, several thousand lawns at a time: The MagPi magazine takes a look at the award-winning IoT sprinkler system of Coolest Projects USA participant Adarsh Ambati.

At any Coolest Projects event, you’re bound to see incredible things built by young makers. At Coolest Projects USA, we had the chance to talk to Adarsh Ambati about his community sprinkler and we were, frankly, amazed.

“The extreme, record-breaking drought in California inspired me to think of innovative ways to save water,” Adarsh tells us. “While going to school in the rain one day, I saw one of my neighbours with their sprinklers on, creating run-offs. Through research, I found that 25% of the water used in an average American household is wasted each day due to overwatering and inefficient watering methods. Thus, I developed a sprinkler system that is compliant with water regulations, to cost-effectively save water for entire neighbourhoods using a Raspberry Pi, moisture sensors, PyOWM (weather database), and by utilising free social media networks like Twitter.”

Efficient watering

In California, it’s very hot year round, so if you want a lush, green lawn you need to keep the grass watered. The record-breaking drought Adarsh was referring to resulted in extreme limitations on how much you could water your grass. The problem is, unless you have a very expensive sprinkler system, it’s easy to water the grass when it doesn’t need to be.

“The goal of my project is to save water wasted during general-purpose landscape irrigation of an entire neighbourhood by building a moisture sensor-based smart sprinkler system that integrates real-time weather forecast data to provide only optimum levels of water required,” Adarsh explains. “It will also have Twitter capabilities that will be able to publish information about when and how long to turn on the sprinklers, through the social networks. The residents in the community will subscribe to this information by following an account on Twitter, and utilise it to prevent water wasted during general-purpose landscaping and stay compliant with water regulations imposed in each area.”

Using the Raspberry Pi, Adarsh was able to build a prototype for about $50 — a lot cheaper than smart sprinklers you can currently buy on the market.

“I piloted it with ten homes, so the cost per home is around $5,” he reveals. “But since it has the potential to serve an entire community, the cost per home can be a few cents. For example, there are about 37000 residents in Almaden Valley, San Jose (where I live). If there is an average of two to four residents per home, there should be 9250 to 18500 homes. If I strategically place ten such prototypes, the cost per house would be five cents or less.”

Massive saving

Adarsh continues, “Based on two months of data, 83% of the water used for outdoor landscape watering can be saved. The average household in northern California uses 100 gallons of water for outdoor landscaping on a daily basis. The ten homes in my pilot had the potential to save roughly 50000 gallons over a two-month period, or 2500 gallons per month per home. At $0.007 per gallon, the savings equate to $209 per year, per home. For Almaden Valley alone, we have the potential to save around $2m to $4m per year!”

The results from Adarsh’s test were presented to the San Jose City Council, and they were so impressed they’re now considering putting similar systems in their public grass areas. Oh, and he also won the Hardware project category at Coolest Projects USA.

The MagPi magazine #83

This article is from today’s brand-new issue of The MagPi, the official Raspberry Pi magazine. Buy it from all good newsagents, subscribe to pay less per issue and support our work, or download the free PDF to give it a try first.

The post IoT community sprinkler system using Raspberry Pi | The MagPi issue 83 appeared first on Raspberry Pi.

AWS Security releases IoT security whitepaper

Post Syndicated from Momena Cheema original https://aws.amazon.com/blogs/security/aws-security-releases-iot-security-whitepaper/

We’ve published a whitepaper, Securing Internet of Things (IoT) with AWS, to help you understand and address data security as it relates to your IoT devices and the data generated by them. The whitepaper is intended for a broad audience who is interested in learning about AWS IoT security capabilities at a service-specific level and for compliance, security, and public policy professionals.

IoT technologies connect devices and people in a multitude of ways and are used across industries. For example, IoT can help manage thermostats remotely across buildings in a city, efficiently control hundreds of wind turbines, or operate autonomous vehicles more safely. With all of the different types of devices and the data they transmit, security is a top concern.

The specific challenges using IoT technologies present has piqued the interest of governments worldwide who are currently assessing what, if any, new regulatory requirements should take shape to keep pace with IoT innovation and the general problem of securing data. This whitepaper uses a specific example to cover recent developments published by the National Institute of Standards and Technology (NIST) and the United Kingdom’s Code of Practice that are specific to IoT.

If you have questions or want to learn more, contact your account executive, or leave a comment below.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.

Author photo

Momena Cheema

Momena is passionate about evangelizing the security and privacy capabilities of AWS services through the lens of global emerging technology and trends, such as Internet of Things, artificial intelligence, and machine learning through written content, workshops, talks, and educational campaigns. Her goal is to bring the security and privacy benefits of the cloud to customers across industries in both public and private sectors.

Build a security camera with Raspberry Pi and OpenCV

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/raspberry-pi-security-camera-opencv/

Tired of opening the refrigerator only to find that your favourite snack is missing? Get video evidence of sneaky fridge thieves sent to your phone, with Adrian Rosebeck’s Raspberry Pi security camera project.

Building a Raspberry Pi security camera with OpenCV

Learn how to build a IoT + Raspberry Pi security camera using OpenCV and computer vision. Send TXT/MMS message notifications, images, and video clips when the security camera is triggered. Full tutorial (including code) here: https://www.pyimagesearch.com/2019/03/25/building-a-raspberry-pi-security-camera-with-opencv

Protecting hummus

Adrian loves hummus. And, as you can see from my author bio, so do I. So it wasn’t hard for me to relate to Adrian’s story about his college roommates often stealing his cherished chickpea dip.

Garlic dessert

“Of course, back then I wasn’t as familiar with computer vision and OpenCV as I am now,” he explains on his blog. “Had I known what I do at present, I would have built a Raspberry Pi security camera to capture the hummus heist in action!”

Raspberry Pi security camera

So, in homage to his time as an undergrad, Adrian decided to finally build that security camera for his fridge, despite now only needing to protect his hummus from his wife. And to build it, he opted to use OpenCV, a Raspberry Pi, and a Raspberry Pi Camera Module.

Adrian’s camera is an IoT project: it not only captures footage but also uses Twillo to send that footage, via a cloud service (AWS), to a smartphone.

Because the content of your fridge lives in the dark when you’re not inspecting it, the code for capturing video footage detects light and dark, and records everything that occurs between the fridge door opening and closing. “You could also deploy this inside a mailbox that opens/closes,” suggests Adrian.

Get the code and more

Adrian provides all the code for the project on his blog, pyimagesearch, with a full explanation of why each piece of code is used — thanks, Adrian!

For more from Adrian, check out his brilliant deep learning projects: a fully functional Pokémon Pokédex and Santa Detector.

The post Build a security camera with Raspberry Pi and OpenCV appeared first on Raspberry Pi.

AWS Online Tech Talks – June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-june-2018/

AWS Online Tech Talks – June 2018

Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

 

Analytics & Big Data

June 18, 2018 | 11:00 AM – 11:45 AM PTGet Started with Real-Time Streaming Data in Under 5 Minutes – Learn how to use Amazon Kinesis to capture, store, and analyze streaming data in real-time including IoT device data, VPC flow logs, and clickstream data.
June 20, 2018 | 11:00 AM – 11:45 AM PT – Insights For Everyone – Deploying Data across your Organization – Learn how to deploy data at scale using AWS Analytics and QuickSight’s new reader role and usage based pricing.

 

AWS re:Invent
June 13, 2018 | 05:00 PM – 05:30 PM PTEpisode 2: AWS re:Invent Breakout Content Secret Sauce – Hear from one of our own AWS content experts as we dive deep into the re:Invent content strategy and how we maintain a high bar.
Compute

June 25, 2018 | 01:00 PM – 01:45 PM PTAccelerating Containerized Workloads with Amazon EC2 Spot Instances – Learn how to efficiently deploy containerized workloads and easily manage clusters at any scale at a fraction of the cost with Spot Instances.

June 26, 2018 | 01:00 PM – 01:45 PM PTEnsuring Your Windows Server Workloads Are Well-Architected – Get the benefits, best practices and tools on running your Microsoft Workloads on AWS leveraging a well-architected approach.

 

Containers
June 25, 2018 | 09:00 AM – 09:45 AM PTRunning Kubernetes on AWS – Learn about the basics of running Kubernetes on AWS including how setup masters, networking, security, and add auto-scaling to your cluster.

 

Databases

June 18, 2018 | 01:00 PM – 01:45 PM PTOracle to Amazon Aurora Migration, Step by Step – Learn how to migrate your Oracle database to Amazon Aurora.
DevOps

June 20, 2018 | 09:00 AM – 09:45 AM PTSet Up a CI/CD Pipeline for Deploying Containers Using the AWS Developer Tools – Learn how to set up a CI/CD pipeline for deploying containers using the AWS Developer Tools.

 

Enterprise & Hybrid
June 18, 2018 | 09:00 AM – 09:45 AM PTDe-risking Enterprise Migration with AWS Managed Services – Learn how enterprise customers are de-risking cloud adoption with AWS Managed Services.

June 19, 2018 | 11:00 AM – 11:45 AM PTLaunch AWS Faster using Automated Landing Zones – Learn how the AWS Landing Zone can automate the set up of best practice baselines when setting up new

 

AWS Environments

June 21, 2018 | 11:00 AM – 11:45 AM PTLeading Your Team Through a Cloud Transformation – Learn how you can help lead your organization through a cloud transformation.

June 21, 2018 | 01:00 PM – 01:45 PM PTEnabling New Retail Customer Experiences with Big Data – Learn how AWS can help retailers realize actual value from their big data and deliver on differentiated retail customer experiences.

June 28, 2018 | 01:00 PM – 01:45 PM PTFireside Chat: End User Collaboration on AWS – Learn how End User Compute services can help you deliver access to desktops and applications anywhere, anytime, using any device.
IoT

June 27, 2018 | 11:00 AM – 11:45 AM PTAWS IoT in the Connected Home – Learn how to use AWS IoT to build innovative Connected Home products.

 

Machine Learning

June 19, 2018 | 09:00 AM – 09:45 AM PTIntegrating Amazon SageMaker into your Enterprise – Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment.

June 21, 2018 | 09:00 AM – 09:45 AM PTBuilding Text Analytics Applications on AWS using Amazon Comprehend – Learn how you can unlock the value of your unstructured data with NLP-based text analytics.

 

Management Tools

June 20, 2018 | 01:00 PM – 01:45 PM PTOptimizing Application Performance and Costs with Auto Scaling – Learn how selecting the right scaling option can help optimize application performance and costs.

 

Mobile
June 25, 2018 | 11:00 AM – 11:45 AM PTDrive User Engagement with Amazon Pinpoint – Learn how Amazon Pinpoint simplifies and streamlines effective user engagement.

 

Security, Identity & Compliance

June 26, 2018 | 09:00 AM – 09:45 AM PTUnderstanding AWS Secrets Manager – Learn how AWS Secrets Manager helps you rotate and manage access to secrets centrally.
June 28, 2018 | 09:00 AM – 09:45 AM PTUsing Amazon Inspector to Discover Potential Security Issues – See how Amazon Inspector can be used to discover security issues of your instances.

 

Serverless

June 19, 2018 | 01:00 PM – 01:45 PM PTProductionize Serverless Application Building and Deployments with AWS SAM – Learn expert tips and techniques for building and deploying serverless applications at scale with AWS SAM.

 

Storage

June 26, 2018 | 11:00 AM – 11:45 AM PTDeep Dive: Hybrid Cloud Storage with AWS Storage Gateway – Learn how you can reduce your on-premises infrastructure by using the AWS Storage Gateway to connecting your applications to the scalable and reliable AWS storage services.
June 27, 2018 | 01:00 PM – 01:45 PM PTChanging the Game: Extending Compute Capabilities to the Edge – Discover how to change the game for IIoT and edge analytics applications with AWS Snowball Edge plus enhanced Compute instances.
June 28, 2018 | 11:00 AM – 11:45 AM PTBig Data and Analytics Workloads on Amazon EFS – Get best practices and deployment advice for running big data and analytics workloads on Amazon EFS.

MagPi 70: Home automation with Raspberry Pi

Post Syndicated from Rob Zwetsloot original https://www.raspberrypi.org/blog/magpi-70-home-automation/

Hey folks, Rob here! It’s the last Thursday of the month, and that means it’s time for a brand-new The MagPi. Issue 70 is all about home automation using your favourite microcomputer, the Raspberry Pi.

Cover of The MagPi 70 — Raspberry Pi home automation and tech upcycling

Home automation in this month’s The MagPi!

Raspberry Pi home automation

We think home automation is an excellent use of the Raspberry Pi, hiding it around your house and letting it power your lights and doorbells and…fish tanks? We show you how to do all of that, and give you some excellent tips on how to add even more automation to your home in our ten-page cover feature.

Upcycle your life

Our other big feature this issue covers upcycling, the hot trend of taking old electronics and making them better than new with some custom code and a tactically placed Raspberry Pi. For this feature, we had a chat with Martin Mander, upcycler extraordinaire, to find out his top tips for hacking your old hardware.

Article on upcycling in The MagPi 70 — Raspberry Pi home automation and tech upcycling

Upcycling is a lot of fun

But wait, there’s more!

If for some reason you want even more content, you’re in luck! We have some fun tutorials for you to try, like creating a theremin and turning a Babbage into an IoT nanny cam. We also continue our quest to make a video game in C++. Our project showcase is headlined by the Teslonda on page 28, a Honda/Tesla car hybrid that is just wonderful.

Diddyborg V2 review in The MagPi 70 — Raspberry Pi home automation and tech upcycling

We review PiBorg’s latest robot

All this comes with our definitive reviews and the community section where we celebrate you, our amazing community! You’re all good beans

Teslonda article in The MagPi 70 — Raspberry Pi home automation and tech upcycling

An amazing, and practical, Raspberry Pi project

Get The MagPi 70

Issue 70 is available today from WHSmith, Tesco, Sainsbury’s, and Asda. If you live in the US, head over to your local Barnes & Noble or Micro Center in the next few days for a print copy. You can also get the new issue online from our store, or digitally via our Android and iOS apps. And don’t forget, there’s always the free PDF as well.

New subscription offer!

Want to support the Raspberry Pi Foundation and the magazine? We’ve launched a new way to subscribe to the print version of The MagPi: you can now take out a monthly £4 subscription to the magazine, effectively creating a rolling pre-order system that saves you money on each issue.

The MagPi subscription offer — Raspberry Pi home automation and tech upcycling

You can also take out a twelve-month print subscription and get a Pi Zero W plus case and adapter cables absolutely free! This offer does not currently have an end date.

That’s it for today! See you next month.

Animated GIF: a door slides open and Captain Picard emerges hesitantly

The post MagPi 70: Home automation with Raspberry Pi appeared first on Raspberry Pi.

Project Floofball and more: Pi pet stuff

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/project-floofball-pi-pet-stuff/

It’s a public holiday here today (yes, again). So, while we indulge in the traditional pastime of barbecuing stuff (ourselves, mainly), here’s a little trove of Pi projects that cater for our various furry friends.

Project Floofball

Nicole Horward created Project Floofball for her hamster, Harold. It’s an IoT hamster wheel that uses a Raspberry Pi and a magnetic door sensor to log how far Harold runs.

Project Floofball: an IoT hamster wheel

An IoT Hamsterwheel using a Raspberry Pi and a magnetic door sensor, to see how far my hamster runs.

You can follow Harold’s runs in real time on his ThingSpeak channel, and you’ll find photos of the build on imgur. Nicole’s Python code, as well as her template for the laser-cut enclosure that houses the wiring and LCD display, are available on the hamster wheel’s GitHub repo.

A live-streaming pet feeder

JaganK3 used to work long hours that meant he couldn’t be there to feed his dog on time. He found that he couldn’t buy an automated feeder in India without paying a lot to import one, so he made one himself. It uses a Raspberry Pi to control a motor that turns a dispensing valve in a hopper full of dry food, giving his dog a portion of food at set times.

A transparent cylindrical hopper of dry dog food, with a motor that can turn a dispensing valve at the lower end. The motor is connected to a Raspberry Pi in a plastic case. Hopper, motor, Pi, and wiring are all mounted on a board on the wall.

He also added a web cam for live video streaming, because he could. Find out more in JaganK3’s Instructable for his pet feeder.

Shark laser cat toy

Sam Storino, meanwhile, is using a Raspberry Pi to control a laser-pointer cat toy with a goshdarned SHARK (which is kind of what I’d expect from the guy who made the steampunk-looking cat feeder a few weeks ago). The idea is to keep his cats interested and active within the confines of a compact city apartment.

Raspberry Pi Automatic Cat Laser Pointer Toy

Post with 52 votes and 7004 views. Tagged with cat, shark, lasers, austin powers, raspberry pi; Shared by JeorgeLeatherly. Raspberry Pi Automatic Cat Laser Pointer Toy

If I were a cat, I would definitely be entirely happy with this. Find out more on Sam’s website.

And there’s more

Michel Parreno has written a series of articles to help you monitor and feed your pet with Raspberry Pi.

All of these makers are generous in acknowledging the tutorials and build logs that helped them with their projects. It’s lovely to see the Raspberry Pi and maker community working like this, and I bet their projects will inspire others too.

Now, if you’ll excuse me. I’m late for a barbecue.

The post Project Floofball and more: Pi pet stuff appeared first on Raspberry Pi.

HackSpace magazine 7: Internet of Everything

Post Syndicated from Andrew Gregory original https://www.raspberrypi.org/blog/hackspace-magazine-7-internet-of-everything/

We’re usually averse to buzzwords at HackSpace magazine, but not this month: in issue 7, we’re taking a deep dive into the Internet of Things.HackSpace magazine issue 7 cover

Internet of Things (IoT)

To many people, IoT is a shady term used by companies to sell you something you already own, but this time with WiFi; to us, it’s a way to make our builds smarter, more useful, and more connected. In HackSpace magazine #7, you can join us on a tour of the boards that power IoT projects, marvel at the ways in which other makers are using IoT, and get started with your first IoT project!

Awesome projects

DIY retro computing: this issue, we’re taking our collective hat off to Spencer Owen. He stuck his home-brew computer on Tindie thinking he might make a bit of beer money — now he’s paying the mortgage with his making skills and inviting others to build modules for his machine. And if that tickles your fancy, why not take a crack at our Z80 tutorial? Get out your breadboard, assemble your jumper wires, and prepare to build a real-life computer!

Inside HackSpace magazine issue 7

Shameless patriotism: combine Lego, Arduino, and the car of choice for 1960 gold bullion thieves, and you’ve got yourself a groovy weekend project. We proudly present to you one man’s epic quest to add LED lights (controllable via a smartphone!) to his daughter’s LEGO Mini Cooper.

Makerspaces

Patriotism intensifies: for the last 200-odd years, the Black Country has been a hotbed of making. Urban Hax, based in Walsall, is the latest makerspace to show off its riches in the coveted Space of the Month pages. Every space has its own way of doing things, but not every space has a portrait of Rob Halford on the wall. All hail!

Inside HackSpace magazine issue 7

Diversity: advice on diversity often boils down to ‘Be nice to people’, which might feel more vague than actionable. This is where we come in to help: it is truly worth making the effort to give people of all backgrounds access to your makerspace, so we take a look at why it’s nice to be nice, and at the ways in which one makerspace has put niceness into practice — with great results.

And there’s more!

We also show you how to easily calculate the size and radius of laser-cut gears, use a bank of LEDs to etch PCBs in your own mini factory, and use chemistry to mess with your lunch menu.

Inside HackSpace magazine issue 7
Helen Steer inside HackSpace magazine issue 7
Inside HackSpace magazine issue 7

All this plus much, much more waits for you in HackSpace magazine issue 7!

Get your copy of HackSpace magazine

If you like the sound of that, you can find HackSpace magazine in WHSmith, Tesco, Sainsbury’s, and independent newsagents in the UK. If you live in the US, check out your local Barnes & Noble, Fry’s, or Micro Center next week. We’re also shipping to stores in Australia, Hong Kong, Canada, Singapore, Belgium, and Brazil, so be sure to ask your local newsagent whether they’ll be getting HackSpace magazine.

And if you can’t get to the shops, fear not: you can subscribe from £4 an issue from our online shop. And if you’d rather try before you buy, you can always download the free PDF. Happy reading, and happy making!

The post HackSpace magazine 7: Internet of Everything appeared first on Raspberry Pi.

Raspberry Jam Cameroon #PiParty

Post Syndicated from Ben Nuttall original https://www.raspberrypi.org/blog/raspberry-jam-cameroon-piparty/

Earlier this year on 3 and 4 March, communities around the world held Raspberry Jam events to celebrate Raspberry Pi’s sixth birthday. We sent out special birthday kits to participating Jams — it was amazing to know the kits would end up in the hands of people in parts of the world very far from Raspberry Pi HQ in Cambridge, UK.

The Raspberry Jam Camer team: Damien Doumer, Eyong Etta, Loïc Dessap and Lionel Sichom, aka Lionel Tellem

Preparing for the #PiParty

One birthday kit went to Yaoundé, the capital of Cameroon. There, a team of four students in their twenties — Lionel Sichom (aka Lionel Tellem), Eyong Etta, Loïc Dessap, and Damien Doumer — were organising Yaoundé’s first Jam, called Raspberry Jam Camer, as part of the Raspberry Jam Big Birthday Weekend. The team knew one another through their shared interests and skills in electronics, robotics, and programming. Damien explains in his blog post about the Jam that they planned ahead for several activities for the Jam based on their own projects, so they could be confident of having a few things that would definitely be successful for attendees to do and see.

Show-and-tell at Raspberry Jam Cameroon

Loïc presented a Raspberry Pi–based, Android app–controlled robot arm that he had built, and Lionel coded a small video game using Scratch on Raspberry Pi while the audience watched. Damien demonstrated the possibilities of Windows 10 IoT Core on Raspberry Pi, showing how to install it, how to use it remotely, and what you can do with it, including building a simple application.

Loïc Dessap, wearing a Raspberry Jam Big Birthday Weekend T-shirt, sits at a table with a robot arm, a laptop with a Pi sticker and other components. He is making an adjustment to his set-up.

Loïc showcases the prototype robot arm he built

There was lots more too, with others discussing their own Pi projects and talking about the possibilities Raspberry Pi offers, including a Pi-controlled drone and car. Cake was a prevailing theme of the Raspberry Jam Big Birthday Weekend around the world, and Raspberry Jam Camer made sure they didn’t miss out.

A round pink-iced cake decorated with the words "Happy Birthday RBP" and six candles, on a table beside Raspberry Pi stickers, Raspberry Jam stickers and Raspberry Jam fliers

Yay, birthday cake!!

A big success

Most visitors to the Jam were secondary school students, while others were university students and graduates. The majority were unfamiliar with Raspberry Pi, but all wanted to learn about Raspberry Pi and what they could do with it. Damien comments that the fact most people were new to Raspberry Pi made the event more interactive rather than creating any challenges, because the visitors were all interested in finding out about the little computer. The Jam was an all-round success, and the team was pleased with how it went:

What I liked the most was that we sensitized several people about the Raspberry Pi and what one can be capable of with such a small but powerful device. — Damien Doumer

The Jam team rounded off the event by announcing that this was the start of a Raspberry Pi community in Yaoundé. They hope that they and others will be able to organise more Jams and similar events in the area to spread the word about what people can do with Raspberry Pi, and to help them realise their ideas.

The Raspberry Jam Camer team, wearing Raspberry Jam Big Birthday Weekend T-shirts, pose with young Jam attendees outside their venue

Raspberry Jam Camer gets the thumbs-up

The Raspberry Pi community in Cameroon

In a French-language interview about their Jam, the team behind Raspberry Jam Camer said they’d like programming to become the third official language of Cameroon, after French and English; their aim is to to popularise programming and digital making across Cameroonian society. Neither of these fields is very familiar to most people in Cameroon, but both are very well aligned with the country’s ambitions for development. The team is conscious of the difficulties around the emergence of information and communication technologies in the Cameroonian context; in response, they are seizing the opportunities Raspberry Pi offers to give children and young people access to modern and constantly evolving technology at low cost.

Thanks to Lionel, Eyong, Damien, and Loïc, and to everyone who helped put on a Jam for the Big Birthday Weekend! Remember, anyone can start a Jam at any time — and we provide plenty of resources to get you started. Check out the Guidebook, the Jam branding pack, our specially-made Jam activities online (in multiple languages), printable worksheets, and more.

The post Raspberry Jam Cameroon #PiParty appeared first on Raspberry Pi.

All Systems Go! 2018 CfP Open

Post Syndicated from Lennart Poettering original http://0pointer.net/blog/all-systems-go-2018-cfp-open.html

The All Systems Go! 2018 Call for Participation is Now Open!

The Call for Participation (CFP) for All Systems Go!
2018
is now open. We’d like to invite you
to submit your proposals for consideration to the CFP submission
site
.

ASG image

The CFP will close on July 30th. Notification of acceptance and
non-acceptance will go out within 7 days of the closing of the CFP.

All topics relevant to foundational open-source Linux technologies are
welcome. In particular, however, we are looking for proposals
including, but not limited to, the following topics:

  • Low-level container executors and infrastructure
  • IoT and embedded OS infrastructure
  • BPF and eBPF filtering
  • OS, container, IoT image delivery and updating
  • Building Linux devices and applications
  • Low-level desktop technologies
  • Networking
  • System and service management
  • Tracing and performance measuring
  • IPC and RPC systems
  • Security and Sandboxing

While our focus is definitely more on the user-space side of things,
talks about kernel projects are welcome, as long as they have a clear
and direct relevance for user-space.

For more information please visit our conference
website
!

AWS IoT 1-Click – Use Simple Devices to Trigger Lambda Functions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-iot-1-click-use-simple-devices-to-trigger-lambda-functions/

We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.

I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:

Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.

Camera ControlTim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.

Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!

Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.

All About AWS IoT 1-Click
As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:

Architects can dream up applications for inexpensive, low-powered devices.

Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.

Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.

Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.

I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.

Who’s Got the Button?
We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.

The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.

The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.

We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at [email protected].

AWS IoT 1-Click Concepts
I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.

Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.

Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.

Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).

Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”

Getting Started with AWS IoT 1-Click
Let’s set up an IoT button using the AWS IoT 1-Click Console:

If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):

The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:

Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:

I am now ready to put them to use. I click on Projects, and then Create a project:

I name and describe my project, and click Next to proceed:

Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):

The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:

I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:

I can inspect my project and see that everything looks good:

I click on the buttons and the SMS messages appear:

I can monitor device activity in the AWS IoT 1-Click Console:

And also in the Lambda Console:

The Lambda function itself is also accessible, and can be used as-is or customized:

As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.

Now Available
I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!

Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.

To learn more, visit the AWS IoT 1-Click home page or read the AWS IoT 1-Click documentation.

Jeff;

 

Amazon Sumerian – Now Generally Available

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-sumerian-now-generally-available/

We announced Amazon Sumerian at AWS re:Invent 2017. As you can see from Tara‘s blog post (Presenting Amazon Sumerian: An Easy Way to Create VR, AR, and 3D Experiences), Sumerian does not require any specialized programming or 3D graphics expertise. You can build VR, AR, and 3D experiences for a wide variety of popular hardware platforms including mobile devices, head-mounted displays, digital signs, and web browsers.

I’m happy to announce that Sumerian is now generally available. You can create realistic virtual environments and scenes without having to acquire or master specialized tools for 3D modeling, animation, lighting, audio editing, or programming. Once built, you can deploy your finished creation across multiple platforms without having to write custom code or deal with specialized deployment systems and processes.

Sumerian gives you a web-based editor that you can use to quickly and easily create realistic, professional-quality scenes. There’s a visual scripting tool that lets you build logic to control how objects and characters (Sumerian Hosts) respond to user actions. Sumerian also lets you create rich, natural interactions powered by AWS services such as Amazon Lex, Polly, AWS Lambda, AWS IoT, and Amazon DynamoDB.

Sumerian was designed to work on multiple platforms. The VR and AR apps that you create in Sumerian will run in browsers that supports WebGL or WebVR and on popular devices such as the Oculus Rift, HTC Vive, and those powered by iOS or Android.

During the preview period, we have been working with a broad spectrum of customers to put Sumerian to the test and to create proof of concept (PoC) projects designed to highlight an equally broad spectrum of use cases, including employee education, training simulations, field service productivity, virtual concierge, design and creative, and brand engagement. Fidelity Labs (the internal R&D unit of Fidelity Investments), was the first to use a Sumerian host to create an engaging VR experience. Cora (the host) lives within a virtual chart room. She can display stock quotes, pull up company charts, and answer questions about a company’s performance. This PoC uses Amazon Polly to implement text to speech and Amazon Lex for conversational chatbot functionality. Read their blog post and watch the video inside to see Cora in action:

Now that Sumerian is generally available, you have the power to create engaging AR, VR, and 3D experiences of your own. To learn more, visit the Amazon Sumerian home page and then spend some quality time with our extensive collection of Sumerian Tutorials.

Jeff;

 

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.

 

 

 

Mayank Sinha’s home security project

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/home-security/

Yesterday, I received an email from someone called Mayank Sinha, showing us the Raspberry Pi home security project he’s been working on. He got in touch particularly because, he writes, the Raspberry Pi community has given him “immense support” with his build, and he wanted to dedicate it to the commmunity as thanks.

Mayank’s project is named Asfaleia, a Greek word that means safety, certainty, or security against threats. It’s part of an honourable tradition dating all the way back to 2012: it’s a prototype housed in a polystyrene box, using breadboards and jumper leads and sticky tape. And it’s working! Take a look.

Asfaleia DIY Home Security System

An IOT based home security system. The link to the code: https://github.com/mayanksinha11/Asfaleia

Home security with Asfaleida

Asfaleia has a PIR (passive infrared) motion sensor, an IR break beam sensor, and a gas sensor. All are connected to a Raspberry Pi 3 Model B, the latter two via a NodeMCU board. Mayank currently has them set up in a box that’s divided into compartments to model different rooms in a house.

A shallow box divided into four labelled "rooms", all containing electronic components

All the best prototypes have sticky tape or rubber bands

If the IR sensors detect motion or a broken beam, the webcam takes a photo and emails it to the build’s owner, and the build also calls their phone (I like your ringtone, Mayank). If the gas sensor detects a leak, the system activates an exhaust fan via a small relay board, and again the owner receives a phone call. The build can also authenticate users via face and fingerprint recognition. The software that runs it all is written in Python, and you can see Mayank’s code on GitHub.

Of prototypes and works-in-progess

Reading Mayank’s email made me very happy yesterday. We know that thousands of people in our community give a great deal of time and effort to help others learn and make things, and it is always wonderful to see an example of how that support is helping someone turn their ideas into reality. It’s great, too, to see people sharing works-in-progress, as well as polished projects! After all, the average build is more likely to feature rubber bands and Tupperware boxes than meticulously designed laser-cut parts or expert joinery. Mayank’s YouTube channel shows earlier work on this and another Pi project, and I hope he’ll continue to document his builds.

So here’s to Raspberry Pi projects big, small, beginner, professional, endlessly prototyped, unashamedly bodged, unfinished or fully working, shonky or shiny. Please keep sharing them all!

The post Mayank Sinha’s home security project appeared first on Raspberry Pi.

Augmented-reality projection lamp with Raspberry Pi and Android Things

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/augmented-reality-projector/

If your day has been a little fraught so far, watch this video. It opens with a tableau of methodically laid-out components and then shows them soldered, screwed, and slotted neatly into place. Everything fits perfectly; nothing needs percussive adjustment. Then it shows us glimpses of an AR future just like the one promised in the less dystopian comics and TV programmes of my 1980s childhood. It is all very soothing, and exactly what I needed.

Android Things – Lantern

Transform any surface into mixed-reality using Raspberry Pi, a laser projector, and Android Things. Android Experiments – http://experiments.withgoogle.com/android/lantern Lantern project site – http://nordprojects.co/lantern check below to make your own ↓↓↓ Get the code – https://github.com/nordprojects/lantern Build the lamp – https://www.hackster.io/nord-projects/lantern-9f0c28

Creating augmented reality with projection

We’ve seen plenty of Raspberry Pi IoT builds that are smart devices for the home; they add computing power to things like lights, door locks, or toasters to make these objects interact with humans and with their environment in new ways. Nord ProjectsLantern takes a different approach. In their words, it:

imagines a future where projections are used to present ambient information, and relevant UI within everyday objects. Point it at a clock to show your appointments, or point to speaker to display the currently playing song. Unlike a screen, when Lantern’s projections are no longer needed, they simply fade away.

Lantern is set up so that you can connect your wireless device to it using Google Nearby. This means there’s no need to create an account before you can dive into augmented reality.

Lantern Raspberry Pi powered projector lamp

Your own open-source AR lamp

Nord Projects collaborated on Lantern with Google’s Android Things team. They’ve made it fully open-source, so you can find the code on GitHub and also download their parts list, which includes a Pi, an IKEA lamp, an accelerometer, and a laser projector. Build instructions are at hackster.io and on GitHub.

This is a particularly clear tutorial, very well illustrated with photos and GIFs, and once you’ve sourced and 3D-printed all of the components, you shouldn’t need a whole lot of experience to put everything together successfully. Since everything is open-source, though, if you want to adapt it — for example, if you’d like to source a less costly projector than the snazzy one used here — you can do that too.

components of Lantern Raspberry Pi powered augmented reality projector lamp

The instructions walk you through the mechanical build and the wiring, as well as installing Android Things and Nord Projects’ custom software on the Raspberry Pi. Once you’ve set everything up, an accelerometer connected to the Pi’s GPIO pins lets the lamp know which surface it is pointing at. A companion app on your mobile device lets you choose from the mini apps that work on that surface to select the projection you want.

The designers are making several mini apps available for Lantern, including the charmingly named Space Porthole: this uses Processing and your local longitude and latitude to project onto your ceiling the stars you’d see if you punched a hole through to the sky, if it were night time, and clear weather. Wouldn’t you rather look at that than deal with the ant problem in your kitchen or tackle your GitHub notifications?

What would you like to project onto your living environment? Let us know in the comments!

The post Augmented-reality projection lamp with Raspberry Pi and Android Things appeared first on Raspberry Pi.

Introducing the AWS Machine Learning Competency for Consulting Partners

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/introducing-the-aws-machine-learning-competency-for-consulting-partners/

Today I’m excited to announce a new Machine Learning Competency for Consulting Partners in the Amazon Partner Network (APN). This AWS Competency program allows APN Consulting Partners to demonstrate a deep expertise in machine learning on AWS by providing solutions that enable machine learning and data science workflows for their customers. This new AWS Competency is in addition to the Machine Learning comptency for our APN Technology Partners, that we launched at the re:Invent 2017 partner summit.

These APN Consulting Partners help organizations solve their machine learning and data challenges through:

  • Providing data services that help data scientists and machine learning practitioners prepare their enterprise data for training.
  • Platform solutions that provide data scientists and machine learning practitioners with tools to take their data, train models, and make predictions on new data.
  • SaaS and API solutions to enable predictive capabilities within customer applications.

Why work with an AWS Machine Learning Competency Partner?

The AWS Competency Program helps customers find the most qualified partners with deep expertise. AWS Machine Learning Competency Partners undergo a strict validation of their capabilities to demonstrate technical proficiency and proven customer success with AWS machine learning tools.

If you’re an AWS customer interested in machine learning workloads on AWS, check out our AWS Machine Learning launch partners below:

 

Interested in becoming an AWS Machine Learning Competency Partner?

APN Partners with experience in Machine Learning can learn more about becoming an AWS Machine Learning Competency Partner here. To learn more about the benefits of joining the AWS Partner Network, see our APN Partner website.

Thanks to the AWS Partner Team for their help with this post!
Randall