As devices proliferate, you may have thousands of deployed models running across your fleets. SageMaker provides Amazon SageMaker Neo, which is the easiest way to optimize ML models for edge devices, enabling you to train ML models one time in the cloud and run them on any device. Although it can provide the necessary scalability and reliability, building such custom solutions comes at the cost of additional maintenance and requires specialized skills.Īmazon SageMaker, together with AWS IoT Greengrass, can help you overcome these challenges. On a smaller scale, you can adopt solutions such as manually logging in to each device to run scripts, use automated solutions such as Ansible, or build custom applications that rely on services such as AWS IoT Core. These are foundational features for any edge application, but creating the infrastructure needed to achieve a secure and scalable solution requires a lot of effort and time. This includes installing applications, deploying application updates, deploying new configurations, monitoring device performance, troubleshooting devices, authenticating and authorizing devices, and securing the data transmission.
#MACHINES CREATOR ANOMALY GAME HOW TO#
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.Īnother common challenge you may face when dealing with computing applications at the edge is how to efficiently manage the fleet of devices at scale. This makes deploying and managing these models more complex. This is primarily due to the hardware, software, and networking restrictions at the edge sites. Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud.
0 kommentar(er)
