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#News(General) [ via IoTGroup ]
While much of the IT focus in the last decade has been on the rise of the cloud edge computing platforms that execute application logic infused with machine learning algorithms represent the next major frontier in IT. Rather than processing data using legacy batch mode processes that require data collected at the edge to be transferred to the cloud or a local datacenter edge computing platforms will analyze and process data in real time at the point where it is being both collected and consumed.
The aggregate results generated by those edge computing applications will then be shared with other applications that are distributed across the enterprise to update for example an enterprise resource planning (ERP) application that is the system of record for the organization. The challenge organizations face is that the dataops processes needed to manage industrial IoT processes at that level of scale still largely don’t exist said Mitchell Ashley CEO and managing analyst for Accelerated Strategies Group a market research and IT consulting firm. Organizations that launch these initiatives will need to create a systematic approach for securely building and deploying applications that are processing and sharing data at levels of scale that are largely unprecedented for them.
One primary reason so many organizations are investing in data lakes is to provide the mechanism through which multiple applications can share access to all that data Ashley noted. Ashley said that once those dataops processes are established organizations will also need to align them with the devops workflows that many organizations now employ to build and deploy applications faster. The data collected will also be needed to train AI models in the cloud which will make use of inference engines to inject AI capabilities into edge computing applications running on platforms that in many cases are essentially mini datacenters. The rise of edge computing is also one of the primary reasons that cloud
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