How businesses can take artificial intelligence to the next level

# Enterprises are increasingly gaining a competitive advantage by deploying artificial intelligence using distributed hybrid cloud architectures.

This is driven by two factors: First, more data is being generated at the edge than ever before. In fact, it is predicted that by 2025, 50% of enterprise-generated data will be processed outside of traditional data centers or cloud computing. A recent global survey found that 78% of IT decision-makers believe moving IT infrastructure to the digital edge is a future-proof priority for their business.

Secondly, moving large amounts of data to artificial intelligence training infrastructure engines in centralized locations for processing means that enterprises will spend valuable time and expense. On top of this, compliance and privacy regulations often require AI data processing and analysis to remain in the country of origin, further justifying the distribution of workloads across multiple countries.

Let’s dive into three different industry use cases where distributed AI is helping businesses save costs, meet regulatory demands and enable new technological advancements.

Gain real-time retail insights while reducing costs

Many large retailers are looking for a competitive advantage by leveraging distributed digital infrastructure strategies. They are using an increasingly popular AI deployment strategy recently identified by IDC: develop AI at the core, such as in a cloud or regional data center, and deploy AI inference models at the edge, then retrain them with new regional data. model to suit the application.

For example, a retailer using a distributed hybrid cloud model might first send its in-store camera information and inventory management data to a hosting metropolitan data center to build regional AI models and leverage federated AI methods to integrate regional models.

It then deploys these optimized AI models to stores to perform low/predictive latency AI model inference to gain insights into inventory, employee shift management, shopper purchasing trend predictions and ad placement recommendations.

Deploying an AI inference engine from a metropolitan data center is more cost-effective than maintaining and servicing these servers at each retail location. This distributed AI infrastructure enables retailers to quickly process and analyze insights in one area, ultimately improving their bottom line.

Maintaining Privacy and Compliance in Video Surveillance

According to UNCTAD, the majority (71%) of the world’s countries have legislation on privacy and data protection . Distributed data management and artificial intelligence architectures can play a key role in helping enterprises ensure compliance.

For example, a large real estate management company with sites in multiple metropolitan areas around the world could leverage a distributed AI architecture for its hundreds of security cameras around the world to maintain visibility by deploying AI where the data is collected. Compliance with local privacy regulations.

Having centralized facilities in the different countries where the business operates ensures that the business does not breach local privacy by sending data to another country that may not have the same compliance regulations as the country where the data originates. Law.

In addition to enabling privacy and data use compliance, this model also reduces costs by hosting the AI ​​inference stack at a single metro location, rather than per facility, even if it is across hundreds of locations Motion detection data is processed on-site at each location.

Autonomous driving through regional updates

Without artificial intelligence infrastructure, autonomous vehicles enabled by advanced driver assistance systems (ADAS) cannot solve certain use cases. ADAS requires artificial intelligence to decide how a vehicle should interact with its surroundings, especially when interacting with vulnerable road users such as cyclists and pedestrians.

The amount of data generated by test vehicles to train artificial intelligence models is huge. For Level 2 and Level 3 ADAS (vehicles can adjust speed, brake and make decisions based on the environment), each vehicle generates daily Data volume ranges from 20TB to 60TB. Artificial intelligence enables connected vehicles to collect and process these large data sets from test fleets faster and more cost-effectively than using traditional infrastructure.

Distributed artificial intelligence infrastructure is defining next-generation vehicle mobility and autonomy. For example, connected vehicles use high-definition maps to provide cars with information about signage and streets.

But what happens when a construction zone or road hazard appears overnight? Instead of requiring each vehicle to handle road hazards individually, distributed AI infrastructure allows these hazards to be sent to an area location and then communicates the hazard to all vehicles in the area.

Follow the flow of data

Nothing feels the gravitational pull of data more than artificial intelligence. To get the most out of their AI infrastructure, enterprises need to evaluate the value of deploying it centrally, regionally or locally. Doing so will save time, money, and valuable latency.

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