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Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial automation, autonomous vehicles, and automated checkouts, require ML models that run on devices at the edge so predictions can be made in real time when new data is available. Another common challenge you may face when dealing with computing applications at the edge is how to efficiently manage the fleet of devices at scale.

AWS IoT Greengrass is software that extends cloud capabilities to local devices. This enables devices to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks. Local devices can also communicate securely with AWS IoT Core and export IoT data to the AWS Cloud. AWS IoT Greengrass developers can use AWS Lambda functions and prebuilt connectors to create serverless applications that are deployed to devices for local execution. The following diagram shows the basic architecture of AWS IoT Greengrass. AWS IoT Greengrass makes it possible for customers to build IoT devices and application logic. Specifically, AWS IoT Greengrass provides cloud-based management of application logic that runs on devices. Locally deployed Lambda functions and connectors are triggered by local events, messages from the cloud, or other sources. In AWS IoT Greengrass, devices securely communicate on a local network and exchange messages with each other without having to connect to the cloud. AWS IoT Greengrass provides a local pub/sub message manager that can intelligently buffer messages if connectivity is lost so that inbound and outbound messages to the cloud are preserved. Through secure connectivity in the local network. Device security credentials function in a group until they are revoked, even if connectivity to the cloud is disrupted, so that the devices can continue to securely communicate locally. MQTT messaging over the local network between devices, connectors, and Lambda functions using managed subscriptions. MQTT messaging between AWS IoT and devices, connectors, and Lambda functions using managed subscriptions. Shadows can be configured to sync with the AWS Cloud. Automatic IP address detection that enables devices to discover the Greengrass core device. Central deployment of new or updated group configuration.

Last week I attended a talk given by Bryan Mistele, president of Seattle-based INRIX. Bryan's talk provided a glimpse into the future of transportation, centering around four principle attributes, often abbreviated as ACES: Autonomous – Cars and trucks are gaining the ability to scan and to make sense of their environments and to navigate without human input. Connected – Vehicles of all types have the ability to take advantage of bidirectional connections (either full-time or intermittent) to other cars and to cloud-based resources. They can upload road and performance data, communicate with each other to run in packs, and take advantage of traffic and weather data. Electric – Continued development of battery and motor technology, will make electrics vehicles more convenient, cost-effective, and environmentally friendly.

Fulfilment & Logistics At Amazon, we've been making investments in ML for the last 20 years… 3. 2017, Amazon Web Services, Inc. or its Affiliates. At Amazon, we've been making investments in ML for the last 20 years… Fulfilment & Logistics Search & Discovery 5. 2017, Amazon Web Services, Inc. or its Affiliates. At Amazon, we've been making investments in ML for the last 20 years… Fulfilment & Logistics Existing Products Search & Discovery 7. 2017, Amazon Web Services, Inc. or its Affiliates. Put machine learning in the hands of every developer and data scientist ML @ AWS: Our mission 14. 2017, Amazon Web Services, Inc. or its Affiliates. What are some of the ML capabilities our customers are asking for?

Last week at the Amazon Web Services' re:Invent conference, AWS and Intel introduced a new video camera, AWS DeepLens, that acts as an intelligent device that can run deep learning algorithms on captured images in real-time. The key difference between DeepLens and any other AI-powered camera lies in the horsepower that makes it possible to run machine learning inference models locally without ever sending the video frames to the cloud. Developers and non-developers rushed to attend the AWS workshop on DeepLens to walk away with a device. There, they were enticed with a hot dog to perform the infamous "Hot Dog OR Not Hot Dog" experiment. I managed to attend one of the repeat sessions, and carefully ferried the device back home.