AWS Bringing Machine Learning Services to Industrial, Manufacturing

Amazon Web Services is rolling out five new offerings to help industrial and manufacturing customers embed intelligence in their production processes to improve operational efficiency, quality control, security, and workplace safety.

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Amazon Web Services is rolling out five new offerings to help industrial and manufacturing customers embed intelligence in their production processes to improve operational efficiency, quality control, security, and workplace safety.


The latest AWS for Industrial offerings, which include multiple services and a new appliance, are designed to address industrial customers' common technical challenges. Altogether, the technologies include machine learning, sensor analysis and computer vision capabilities. 


The AWS purpose-built offerings are designed to be "easy to install, deploy, and get up and running quickly and that connect the cloud to the edge to help deliver the smart factories of the future for our industrial customers," said Swami Sivasubramanian, vice president of Amazon Machine Learning for AWS, in a statement. 


The AWS offerings include: Amazon Monitron, Amazon Lookout for Equipment, the AWS Panorama Appliance, the AWS Panorama SDK, and Amazon Lookout for Vision services. 


"Industrial and manufacturing customers are constantly under pressure from their shareholders, customers, governments, and competitors to reduce costs, improve quality, and maintain compliance. These organizations would like to use the cloud and machine learning to help them automate processes and augment human capabilities across their operations, but building these systems can be error-prone, complex, time-consuming, and expensive," Sivasubramanian added. 


Here's a more detailed look at the latest AWS Industrial Machine Learning Services:


Amazon Monitron: This service offers an end-to-end machine monitoring system comprised of sensors, a gateway, and a machine learning service to detect anomalies and predict when industrial equipment will require maintenance. 


AWS added the Monitron service enables customers "to remove cost and complexity from building a sophisticated, machine learning-driven predictive maintenance system from scratch," allowing users to focus on core manufacturing, supply chain, and operations functions. In specific, it detects when machines are not operating normally based on abnormal fluctuations in vibration or temperature and notifies customers when to examine machinery to determine if preventative maintenance is needed.  


In launching Amazon Monitron, AWS officials noted in their official announcement the historical and ongoing lack of smart and efficient solutions to help industrial firms perform ongoing equipment maintenance.

Reactive maintenance can result in significant costs and downtime, while preventive maintenance can be costly, result in over-maintenance, or fail to prevent breakdown if not performed often enough. Predictive maintenance (the ability to foresee when equipment is likely to need maintenance) is a more promising solution. However, in order to make it work, companies have historically needed skilled technicians and data scientists to piece together a complex solution from scratch. 


This included identifying and procuring the right type of sensors for the use case and connecting them together with an IoT gateway (a device that aggregates and transmits data). Companies then had to test the monitoring system and transfer the data to on-premises infrastructure or the cloud for processing. Only then could the data scientists on staff build machine learning models to analyze the data for patterns and anomalies, or create an alerting system when an outlier was detected. 


Some companies have invested heavily in installing sensors across their equipment and the necessary infrastructure for data connectivity, storage, analytics, and alerting. But even these companies typically use rudimentary data analytics and simple modeling approaches that are expensive and often ineffective at detecting abnormal conditions compared to advanced machine learning models. 


Most companies lack the expertise and staff to build and refine the machine learning models that would enable highly accurate predictive maintenance. As a result, few companies have been able to successfully implement predictive maintenance, and those that have done it are looking for ways to further leverage their investment, while also easing the burden of maintaining their homegrown solutions. 

Amazon Lookout for Equipment:  This service provides a way to send their sensor data to AWS to build models for them and return predictions to detect abnormal equipment behavior. To get started, customers upload their sensor data to Amazon Simple Storage Service (S3) and provide the S3 location to Amazon Lookout for Equipment. It can analyze data, assess normal or healthy patterns, and then use learnings to build a customized model for the customer's environment. 


The AWS Panorama Appliance:  This Amazon hardware allows organizations to add computer vision to existing on-premises cameras that customers may already have deployed. Customers start by connecting the AWS Panorama Appliance to their network, and the device automatically identifies camera streams and starts interacting with the existing industrial cameras. The AWS Panorama Appliance is integrated with AWS machine learning services and IoT services that can be used to build custom machine learning models or ingest video for more refined analysis. 


The AWS Panorama Software Development Kit (SDK): This toolkit aims to help hardware vendors to build new cameras that can run meaningful computer vision models at the edge. Cameras built with the AWS Panorama SDK run computer vision models for use cases like detecting damaged parts on a fast-moving conveyor belt or spotting when machinery is outside of a designated work zone.


These cameras can use chips designed for computer vision from NVIDIA and Ambarella. Using the AWS Panorama SDK, manufacturers can build cameras with computer vision models that can process higher quality videos with better resolution for spotting issues. They can also build more sophisticated models on low-cost devices that can be powered over Ethernet and placed around a site. 


Amazon Lookout for Vision: This service offers customers a high accuracy, low-cost anomaly detection solution that uses machine learning to process thousands of images an hour to spot defects and anomalies.


Customers send camera images to Amazon Lookout for Vision in batch or in real-time to identify anomalies, such as a crack in a machine part. It then reports the images that differ from the baseline so that appropriate action can be taken. Amazon Lookout for Vision is sophisticated enough to handle variances in camera angle, pose, and lighting arising from changes in work environments.


AWS offers over 175 fully featured services for compute, storage, databases, networking, analytics, robotics, AI/ML, IoT (Internet of Things), mobile, security, app development and deployment and more.