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Original equipment manufacturers (OEMs) in the automotive industry are facing a rude awakening. In this new world, automotive telemetry and in-vehicle functionality take precedence over car looks and build quality. Implications for Automotive Data Storage. The Transition from Engineering to Data.
The Car as Code: Software Drives the Automotive Industry by Pure Storage Blog Let’s start with a bold quote: “Once, software was a part of the car. To help alleviate these automotive industry challenges, automakers are building their own, internal platforms. What Are the Biggest Disruptors in the Automotive Industry?
The in-car experience has come a long way since the early days of automotive transportation. To accelerate innovation, automotive companies have had to adopt software-driven business models to meet current and future market expectations. . Any excess capacity gets re-distributed to support ongoing development needs. .
Civil protection, in the form of locally-based disaster response capacity, would begin to emerge in the following decade, which would end with the inauguration of the United Nations Decade for Natural Disaster Reduction. Nonetheless, there were some surprising victories in the food processing and automotive sectors.
It has many potential applications in the automotive industry. . The Right Platform for Automotive AI . To help automakers realize the many promises of automotive AI, Pure and NVIDIA have created AIRI//S ™, a complete AI technology stack. This is where artificial intelligence (AI) can play a key role.
It’s crunch time for automotive industry original equipment manufacturers (OEMs). Similarly, a data analysis sweep could pick up on a robot that is operating well below its capacity. This is what allows an automotive OEM to finally connect IT/ OT data pulled from the production line with the growing IT/OT data set onboard the car.
There are so many practical applications for this technology that it’s no surprise AI is now supporting mainstream use cases in industries from healthcare and life sciences, to semiconductor and chip manufacturing, to automotive, financial services, and beyond. Challenges of AI Implementation.
Read on for more Western Digital Announces New Enterprise Storage Lineup The products are intended to drive storage forward in the data center, client, automotive, and consumer segments.
FlashBlade//S handles it all—from workloads that require the highest levels of flash performance to capacity-optimized disk-based ones that call for hybrid density and efficiency. Pure Storage is helping customers deliver on their AI goals across many industries, including automotive , biotech , genomics , and more.
Autonomous and semi-autonomous vehicles: In the automotive industry, AI enables self-driving cars to recognize lane markers and traffic lights and determine when they can change lanes. Scale Capacity and Performance with AIRI//S. They use data to interpret questions and supply answers. . Machine Learning.
and 77% (automotive and other industries) to 82% (telecom). Half of the respondents worldwide (50%) said that generative AI would require them to rethink their unstructured data capacity; 49% said generative AI would change how the business approaches the cloud operating model.
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