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Currently, businesses have been able to create an impressive competition by widely applying artificial intelligence technology, which can be said to be due to two factors:
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First That more data is generated at the edge than ever before. In this regard, it is predicted that by 2025, 50% of data generated by organizations will be processed in a non-traditional environment. On the other hand, a recent global survey showed that 78% of IT decision makers consider moving their IT infrastructure to the digital edge a priority to future-proof their business.
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Second When large sets of data are fed into AI training infrastructure engines for processing, it can only mean one thing, and that is that businesses are spending valuable time and money on this. In addition, compliance and privacy laws mandate that the processing and analysis of AI data be kept in the country of origin, and this usually justifies the distribution of the workload across countries.
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Here, we’re going to explore three industry use cases where distributed AI can help organizations save money, meet regulatory needs, and be successful in terms of achieving new technology advancements:
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Gain retail insight and reduce costs
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Many large retailers have been able to create positive competition by using the distributed digital infrastructure method. They use the more popular AI deployment strategy, something IDC recently covered. In other words, developing AI at the core and then deploying the final AI model at the edge and finally retraining this model with new regional data.
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Let’s take an example to clarify: a retailer using a distributed hybrid cloud model might first send its indoor camera feeds and management data to a metro data center in real-time, so that it can create regional AI models and have been able to Use integrated artificial intelligence methods to integrate regional models. It then applies AI models optimized for storage locations, to apply such model’s conclusions to information about inventory, employee shift management, predicting customer buying trends and ad placement recommendations.
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In fact, deploying AI inference engines in a metro data center is more cost-effective than maintaining and servicing these servers in retail locations. Such an infrastructure enables retailers to process their information quickly in a single place and ultimately improve the bottom line.
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Privacy and compliance in video surveillance
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Most countries have enacted laws regarding privacy and data protection. This issue can play a key role in helping organizations. For example, a large real estate management company, with sites located in several metro areas around the world, can use a distributed AI architecture for its hundreds of security cameras and protect privacy by deploying AI where data is collected. keep In fact, having centralized facilities in different countries will ensure that local privacy laws are not violated in other countries that have similar regulations in this regard. This model, in addition to achieving privacy and data usage, greatly reduces costs.
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Enable auto-driving via regional updates
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Self-driving vehicles enabled by Advanced Driver Assistance Systems (ADAS) cannot operate without an AI infrastructure. In fact, ADAS requires artificial intelligence to make decisions about how the vehicle interacts with its surroundings, especially with vulnerable users such as cyclists and pedestrians. In fact, AI allows connected vehicles to collect and process data from test fleets faster than traditional infrastructure.
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The truth is that the distributed AI infrastructure will somehow introduce the next generation of cars. For example, connected vehicles use HD maps that give the car information about signs and streets; Now imagine a road hazard or construction zone appearing on the road overnight! Now, instead of each vehicle processing the road hazard individually, the distributed AI infrastructure allows new hazards to be sent to a specific area and then propagates the hazards to all vehicles in the area.
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Going with the flow of data
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Nothing feels the gravitational pull of data like AI. For this purpose, in order to make maximum use of artificial intelligence infrastructure, organizations should evaluate the value of deploying them centrally, regionally, or locally, which saves time and money.
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