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How to Apply Machine Learning in Data Centers
Data center vendors are steadily making it easier for their customers to begin using AI/ML by embedding the technology in their products. One example is specialized silicon designed to perform complex mathematical and computational tasks in a more efficient way.
Fremont, CA: Artificial Intelligence (AI) and Machine Learning (ML) technologies are a tried and true method for data center operators to maximize uptime, optimize energy usage, detect potential risks quickly, and defend against cyber-attacks.
Major hyperscalers, for instance, have developed in-house AI to support use cases such as cooling. However, by leveraging AI-as-a-Service on cloud platforms, smaller operators can reap the benefits of AI/ML as well.
Data center vendors are also steadily making it easier for their customers to begin using AI/ML by embedding the technology in their products. One example is specialized silicon designed to perform complex mathematical and computational tasks in a more efficient way. Most AI use cases today are very narrow, so these AI chips can be trained for specific tasks such as pattern recognition, natural language processing, network security, robotics, and automation.
AI is maturing, which means its capabilities are growing at the same time it rides down the cost curve. Those two trends will enable data center vendors to embed AI/ML into more of their products. For example, RISC-V and other open-source technologies are lowering the barriers to purpose-built “building blocks” that can focus on efficiency, performance, and scalability like never before. That, in turn, will drive even more adoption and use cases, including among smaller data center operators that currently consider AI/ML too expensive to implement widely or at all.
Use Digital Twins
Digital twins also are worth considering with data center design and management, so the 3D virtual replica can simulate its physical behavior under any operating scenario. It encompasses the entire data center ecosystem, including virtual representations of the facility’s building blocks: the power, cooling, and IT system components from all major OEMs.
The digital twin brings all stakeholders together to strategize and take control of the performance and business impact of operations on your data center. The digital twin provides empowering visibility to reduce operational risk, remove process bottlenecks and enable the analysis of “what-ifs” - all in one system.
Rather than using an exclusively data-driven model, data center digital twins are also physics-based, with the ability to simulate the performance of a new configuration. A physics-based digital twin consists of a full 3D representation of the data center space, architecture, mechanical and engineering systems, cooling, power connectivity, and the raised floor’s weight-bearing capability. This enables operators to predict, visualize and quantify the impact of any change in the data center prior to implementation, empowering them to make decisions with confidence.