AIOps and autonomous driving: New lessons for IT infrastructure

[Ronak_Chokshi]

Advanced AI features are built into every modern car. IT organizations that deploy a similar end-to-end AIOps framework can improve uptime and unlock cloud operational agility. 

Clearly, AI has brought enormous changes to modern transportation systems. Every year, vehicles become safer, more reliable, and more driver friendly thanks to technology improvements such as adaptive cruise control, collision mitigation braking, and autonomous parallel parking. In many ways, cars have evolved into mobile computers: edge systems equipped with navigation tools and sensors that are connected to the cloud. This evolution in automotive technology shows no signs of slowing down. The continual exchange of real-time information between today’s cars and analytical databases in the cloud train machine learning models that will someday enable fully autonomous vehicles.

It’s time for your IT organization to deploy infrastructure that offers the same cloud operational agility – infrastructure with fully autonomous operations that will make your organization more agile and efficient, and increase uptime. HPE’s edge-to-cloud, AI-driven infrastructure operates in much the same way as modern transportation systems: collecting data from storage infrastructure devices, using this data to train machine learning models in the cloud, and deploying the final machine learning model back to the devices. In essence, it’s an enterprise storage system with embedded AI and machine learning in the cloud – that together forms an autonomous AIOps framework.

Read Sandeep Singh’s CIO article to learn how AIOps works from edge to cloud, and discover the three essential elements in an AIOps framework: Cloud agility and autonomous operations: How AIOps works from edge to cloud

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Ronak Chokshi
Hewlett Packard Enterprise

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