Like every other event this year, one of the most awaited cloud computing events, AWS re:Invent 2020 is also being held virtually. The three-week virtual event took place from November 30th with live keynotes from Andy Jassy, CEO of AWS and Werner Vogels, VP and CTO of Amazon.com. From the very first day, the event has started to announce the major launch and preview announcements including machine learning tools, containers and more. Below here, we have listed the latest announcements on AI and machine learning, in no particular order, made at AWS re:Invent 2020. After Inferentia, AWS launched its second custom machine learning (ML) chip known as Trainium.
Amazon Web Services is rolling out a series of new tools within its industrial Internet of things lineup that aim to improve machine performance and uptime. First up the company announced Monitron, a condition monitoring service for customers that currently lack an existing sensor network. The system and its array of sensors can detect potential failures on critical equipment, allowing for the implementation of a predictive maintenance program. For those customers that do have an existing sensor network, AWS introduced an API-based machine learning (ML) service called Lookout for Equipment that functions as a pathway to send sensor data to AWS for predictive modeling. Like Monitron, Lookout for Equipment analyzes sensor data to detect abnormal behavior on industrial machines.
Yesterday at AWS re:Invent 2020, we announced AWS Panorama, a new machine learning (ML) Appliance and SDK, which allows organizations to bring computer vision (CV) to their on-premises cameras to make automated predictions with high accuracy and low latency. In this post, you learn how customers across a range of industries are using AWS Panorama to improve their operations by automating monitoring and visual inspection tasks. For many organizations, deriving actionable insights from onsite camera video feeds to improve operations remains a challenge, whether it be increasing manufacturing quality, ensuring safety or operating compliance of their facilities, or analyzing customer traffic in retail locations. To derive these insights, customers must monitor live video of facilities or equipment, or review recorded footage after an incident has occurred, which is manual, error-prone, and difficult to scale. Customers have begun to take advantage of CV models running in the cloud to automate these visual inspection tasks, but there are circumstances when relying exclusively on the cloud isn't optimal due to latency requirements or intermittent connectivity.
With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy with anomaly detection, help provide continuous improvements, and retrain models with updated predictions. However, you're often forced to choose between an ML-only or human-only system. Companies are looking for the best of both worlds, integrating ML systems into your workflow while keeping a human eye on the results to achieve higher precision.
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