Amazon IoT Greengrass makes it easy to perform machine learning inference locally on devices, using models that are created, trained, and optimized in the cloud. IoT Greengrass gives you the flexibility to bring your own pre-trained model stored in Amazon S3.
Machine learning uses statistical algorithms that learn from existing data, a process called training, in order to make decisions about new data, a process called inference. During training, patterns and relationships in the data are identified to build a model. The model allows a system to make intelligent decisions about data it hasn’t encountered before. Optimizing models compresses the model size so it runs quickly. Training and optimizing machine learning models require massive computing resources, so it is a natural fit for the cloud. But, inference takes a lot less computing power and is often done in real-time when new data is available. Getting inference results with very low latency is important to making sure your IoT applications can respond quickly to local events.
IoT Greengrass gives you the best of both worlds. You use machine learning models that are built, trained, and optimized in the cloud and run inference locally on devices. For example, you can build a predictive model for scene detection analysis, optimize it to run on any camera, and then deploy it to predict suspicious activity and send an alert. Data gathered from the inference running on IoT Greengrass can be sent back to the cloud where it can be tagged and used to continuously improve the quality of machine learning models.
Deploy models to your connected devices with a few clicks
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How it works
Retail and hospitality
Predictive industrial maintenance
Yanmar leverages Amazon IoT Greengrass ML Inference as part of their IoT precision agriculture solution that increases the intelligence of greenhouse operations by automatically detecting and recognizing the main growth stages of vegetables.
Amazon IoT Greengrass ML Inference enabled IoT devices allows DFDS to predict and optimize ship propulsion, ultimately reducing fuel consumption for their entire fleet.
“The pervasiveness of artificial intelligence and the pace of digital transformation continues to grow at an astonishing rate. Innovations like the newest improvements to Amazon IoT Greengrass ML Inference that markedly decrease latency without decreasing the accuracy of ML inference accelerate new solutions to emerging industrial automation use cases for object identification and classification. Amazon Web Services’ new machine learning solution integrated with Leopard Imaging’s AICam powered by NVIDIA® GPU will be a cornerstone in any edge to cloud Industrial and Smart City solution.”
-Bill Pu, President and Co-Founder, Leopard Imaging
"The potential of computer vision use cases enabled by IoT and AI is vast for businesses to exponentially improve productivity and efficiency. In this time of intelligent transformation, our premium industrial Think IoT cameras powered by Amazon IoT Greengrass with the latest machine learning upgrades are engineered to make a notable difference to enterprise customers.”
- Jon Pershke, Vice President of Strategy and Emerging Business, Intelligent Devices
“Vieureka of Panasonic is very pleased to utilize the evolving functions of Amazon Web Servivces’ machine learning as enabled by Amazon IoT Greengrass. In order to offer Vieureka-Cameras and service management functions to all the partners of the Amazon Web Servivces community, I would like to develop an Amazon IoT Greengrass compatible version as soon as possible. We will create the environment for developers in the spring of 2019, with commercial versions available in autumn of the same year.”
- Miyazaki, CEO of Vieureka Service, Panasonic
“The addition of Amazon IoT Greengrass with its latest ML Inference update running on ADLINK’s industrial vision systems makes for truly plug-and-play IoT. Now when we power-on an off-the-shelf ADLINK NEON smart camera running Amazon IoT Greengrass with its latest ML Inference update, we can get to high-quality outcomes much, much faster. This allows us to further speed development of our IoT digital experiments for our logistics, quality inspection, industrial robotics, and other manufacturing customers.”
- Elizabeth Campbell, General Manager, The Americas, ADLINK Technology