How To Implement Machine Learning In Data Center

AI can help with cooling, and a digital twin can help with construction outcomes.

FREMONT, CA: Artificial Intelligence (AI) and Machine Learning (ML) technologies are a tried and true method for data center operators to increase uptime, minimize energy use, discover possible dangers promptly, and fight against cyber-attacks. Major hyperscalers, for example, have built in-house AI to serve use cases such as cooling. However, by utilizing AI-as-a-Service on cloud platforms, smaller operators may also reap the benefits of AI/ML.

• Embedded AI

By integrating AI/ML technology in their products, data center providers make it easier for their clients to start adopting AI/ML. Specialized silicon, for example, is intended to handle complex mathematical and computing tasks more efficiently. In addition, because most AI use cases are highly technical today, these AI chips may get taught for a specific job such as pattern recognition, natural language processing, network security, robotics, and automation.

AI is maturing, which implies that its capabilities are expanding while its costs are decreasing. These two trends will enable data center companies to include AI/ML into a broader range of their products.

• Implementing AI/ML In the new construction and retrofits

Building information modeling (BIM) and building performance simulation (BPS) technologies, for example, should be used by operators to integrate AI/ML into their planning and construction processes. The guidance is applicable to retrofit initiatives, such as allowing predictive maintenance at an existing plant.

• Leverage digital twins

Digital twins should also be included in data center architecture and administration so that the 3D virtual replica may replicate its physical behavior under any operational situation. In addition, it has virtual representations of the facility's building blocks: power, cooling, and IT system components from all major OEMs.

The digital twin combined with AI can assist IT teams in dealing with the increasing complexity of current data center settings. While data centers are the essential performance hubs at the heart of the digital world, operations still need a significant amount of physical labor and in-depth, specialized knowledge to keep things operating.