AI and ML: The new frontier of innovation and optimization in the data center

Data center infrastructure often struggles to align current and projected IT loads with their critical infrastructure, resulting in mismatches that threaten their ability to meet escalating demands. Against this backdrop, traditional data center approaches must be modified.

Data centers are now integrating artificial intelligence (AI) and machine learning (ML) technologies into their infrastructure to stay competitive. By implementing an AI-driven layer within traditional data center architecture, enterprises can create autonomous data centers that can optimize and perform common data engineering tasks without human intervention.

Using AI to Power Traditional Architectures

In recent years, the proliferation of AI and ML technologies in the data center has been dramatic. Artificial intelligence is driving efficiency and performance across a variety of use cases.

Nisum executive vice president Sajid Mohamedy said AI-driven data centers can help organizations gain a competitive advantage by optimizing application performance and availability, which in turn helps increase customer satisfaction and loyalty. Adding AI to the mix helps optimize resource allocation, thereby increasing data center efficiency and reducing costs. ”

Rapid fault detection and prediction, root cause analysis, power usage optimization and resource capacity allocation optimization are just a few examples of deploying data and algorithm-driven technologies to maximize data center efficiency.

As outages become more frequent and expensive, integrating artificial intelligence into the data center is becoming increasingly necessary for every data-driven enterprise. AI-driven data centers offer a range of benefits, among which The main one is the potential to reduce downtime and improve overall system reliability, ultimately resulting in significant cost savings for the organization.

Improving fault detection and prediction capabilities

Ellen Campana, Head of Artificial Intelligence at KPMG U.S. Artificial intelligence has historically been used to enhance data storage optimization, energy utilization and accessibility. However, in recent years, there has been a clear trend in extending the utility of AI to fault detection and prediction, which can trigger self-healing mechanisms.

"The key to simplifying automated detection is to provide the AI ​​with a window into the details of hardware and software operations, including network traffic. If traffic within a node slows down, AI can detect the pattern and trigger a restart of processes or the entire node. ”

Pratik Gupta, chief technology officer of IBM Automation, believes that AI has the potential to transform across data centers and hybrid cloud environments. By enhancing the user experience in applications, simplifying operations and enabling CIOs and business decision-makers to benefit from a range of Insights are gleaned from data, and artificial intelligence drives innovation and optimization.

Clear understanding of application resource levels

Gupta said IBM expects data center energy consumption to increase by 12% by 2030 ( or more), due to the expiration of Moore’s Law and the explosion of data volume, speed, and energy-intensive workloads.

“Simply put, AI can reduce the need to buy, maintain, manage, and The amount of hardware to monitor. "

Gupta said data center managers must have a clear understanding of their organization's application resource levels in order to flexibly scale to meet real-time demands. AI-driven automation can play a key role in this process, reducing resource risks of congestion and latency, while ensuring hardware workloads remain secure and performance standards are maintained.

For example, IBM’s Turbonomic can automatically optimize application resource levels and scale based on business needs.

Gupta said : “This enables IT managers to have a single dashboard to oversee resource levels, make decisions in real-time and increase efficiency as it ensures their applications are not over-provisioned. ”

Maximizing the Benefits of an AI-Powered Data Center

AI and ML use cases in data centers continue to grow, but organizations must consider some key factors before implementation. While prepackaged AI and ML solutions are increasingly available, but there is still a need for integration that goes beyond single point solutions. DIY AI deployments are possible, but require investments in sensors to collect the data and expertise to turn the data into usable insights.

Campana said: “Many organizations choose to implement their own data centers precisely because they can ensure that data is not pooled with other people’s data or used in ways they cannot control. While this is true, organizations must accept responsibility for maintaining security and privacy. ”

With the right resources, data centers can become smarter and more efficient, but achieving this goal requires optimal planning.

Gupta said: “Planning should be a key pillar in implementing an AI-driven data center. Successful deployment does not happen overnight and requires a lot of iteration and thought before rollout. There are factors that IT leaders need to consider, Such as understanding what hardware can and should be retained, and which workloads need to be moved to the cloud.” . This means identifying the right use cases for AI and ML, investing in the necessary infrastructure and tools, and developing a team of skilled employees to effectively manage and maintain the system.

Gupta said enterprises often maintain huge infrastructures, from distributed data center locations to various cloud deployments. IT leaders need to consider whether they need to build an aggregation lake for all data sources...or bring data preparation, ML and AI tools to each location. As enterprises transform their IT infrastructure, they must consider not only the value delivered, but also the vulnerabilities created. ”

He added: “The best plans can go wrong. The same is true for technology rollout, where nimble organizations that can quickly adjust course will be more successful. "

Four emerging strategies to improve IT and data center performance

AIOps, MLOps, DevOps and SecOps each have their own unique advantages. When combined, they can optimize data center operations and broader IT performance, lower costs and enable service improvements.

AIOps automates and scales enterprise-wide data center and IT workflows

AIOps is becoming the key to enterprise sustainability and carbon footprint in the data center is at the heart of emissions reduction efforts and has been proven effective in identifying the causes of performance gaps. At the heart of this technology is its ability to interpret and recommend actions based on real-time performance data (causal analysis).

For example, Walmart is Use AIOps to streamline e-commerce operations. AIOps relies on a combination of ML models and natural language processing (NLP) to discover new process workflows that can improve the accuracy, cost-effectiveness, and efficiency of data center operations. Retailers also use AIOps to detect and Address inefficiencies and disconnected processes while enabling technology stack automation and broader infrastructure management.

AIOps enables more accurate real-time anomaly detection within e-commerce platforms. The technology is also good at correlating data from Center data from all available sources to provide a 360-degree view of operations and identify where availability, cost control and performance can be improved.

Retailers rely on DevOps to accelerate application development

Retailers Rely on DevOps to stay competitive and reduce time to market for new applications and features. DevOps is based on a software development methodology that emphasizes collaboration and communication between software developers and IT operations teams. It is instrumental in streamlining new mobile apps, website functionality, and customer experience-based The enhanced aspects of software delivery and development are proven to be effective.

Amazon, Target, Nordstrom, Walmart and other leading retailers have adopted DevOps as their primary software development process. Retail CIOs say the quality of DevOps code bases The higher, the more efficiently the data center operates, delivering the latest application versions to customers around the world.

MLOps provides a lifecycle-based approach

As retailers recruit more For data scientists, MLOps becomes as important as DevOps to keep models current and usable. MLOps applies DevOps principles to ML models and algorithms. Leading retailers use MLOps to design, test, and release new models to improve customers Segmentation, demand forecasting and inventory management.

Starting with inventory management and optimization, MLOps is proven to effectively solve the most costly and challenging problems in retail. Supply chain uncertainty, long-term Labor shortages and rising inflationary costs have made inventory management critical to a retailer's success.

Macy, Walmart and others are using MLOps to optimize pricing and inventory management, helping retailers make cost-cutting decisions decisions and protect yourself from the downside risk of holding too much inventory.

SecOps relies on AI and ML to protect every identity and threat surface

SecOps ensures data centers and broader IT infrastructure remain secure and complaint-free. Zero Trust security assumes that no user or device can be trusted and every identity must be verified, which is the foundation of any successful SecOps implementation. The goal is to reduce the attack surface and risk of increasingly sophisticated cyberattacks.

SecOps optimizes data center security by combining the most proven technologies to reduce intrusions and breaches. Adopting zero-trust security helps retailers protect the identities of their customers, employees and suppliers, while micro-segmentation can limit the blast radius of any attack.

The future of artificial intelligence and data center technology

Edge computing is becoming one of the most promising technologies for developing artificial intelligence-driven data centers. By processing data closer to the source, edge computing reduces latency and improves overall performance. When combined with artificial intelligence, the technology offers the potential to enable real-time analysis and decision-making capabilities, enabling data centers to handle the mission-critical processes of the future.

Campana said: “The shift to 5G is an important step in this transformation and is driving a wave of innovation in artificial intelligence-based software infrastructure. For enterprises starting new data centers, it is worth considering their adoption of 5G and their support for Timetable for additional updates to end-user hardware."

And Gupta believes that data intelligence automation is a way to continue to enter the strictly regulated industry, as artificial intelligence and data center tools will be designed to automatically meet compliance Require.

“As artificial intelligence and automation are further embedded in data centers, they will be able to meet the most stringent compliance protocols.”

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