6 Reasons Why You Need AI & ML in Your Data Center

From ensuring a better customer experience to delivering on sustainability initiatives, your data center needs to be smarter

Divesh Kumar
6 Reasons Why You Need AI & ML in Your Data Center

Editor’s note: This blog was originally published in July 2022. It has been updated to include the latest information.

What if your data center was smart enough to help you proactively optimize your operations, such as automatically alerting you when systems are over-consuming power or need to be updated? AI and ML technologies are making digital infrastructure smarter and more intuitive across industries.

At Equinix, we’ve made significant investments in AI/ML technologies and techniques to optimize our International Business Exchange™ (IBX®) data centers and global business operations. AI/ML-based development helps us expedite product and service delivery, empowers us to reach our sustainability goals, and arms us with insights that drive efficiency improvements.[1]

Let’s look at how AI/ML models are creating real value for data center operators and their users in these six areas.

1. Energy efficiency and sustainability

In the global fight against climate change, businesses are working to reduce the environmental impact of their IT infrastructure. Sustainability has become such a priority that investors, employees, partners and customers are now considering a company’s sustainability rating before getting involved with them.

Power usage effectiveness (PUE) is a ratio that compares total facility power with energy consumed by IT equipment. A lower PUE represents greater efficiency, with less energy dedicated to non-IT workloads like cooling and lighting. AI/ML models can help optimize a data center’s PUE by:

  • Proactively managing efficiency and assessing the impact of ongoing manual changes in the operating parameters of data center assets.
  • Identifying the best set of operating parameters for assets to minimize energy consumption while achieving the desired physical impact from each asset (such as maintaining optimal temperature and humidity).

Meeting data center energy demands while achieving sustainability objectives presents IT operators with unique challenges: from finding clean and renewable energy sources to managing power and cooling through adaptive intelligent control systems. At Equinix, we’re advancing a bold environmental agenda through a range of commitments. These include reaching our goal of 100% renewable energy coverage across our global portfolio (we reached 96% in 2022) and becoming climate-neutral in our global operations by 2030.

We use AI/ML to develop PUE optimization models that recommend optimal operating parameters for assets based on a state-of-the-asset digital twin (a digital model representing a physical asset). Through these models, we can forecast power and space capacity in our IBX data centers and ensure we meet customer requirements for specific megawatt thresholds.

We’re also incorporating greater visibility into our customers’ cabinet-level energy consumption via SmartView APIs. Our customers can move their IT into a ready-made sustainable data center with a digital infrastructure platform that gives them valuable data. In turn, this data can help them regulate their own energy usage and meet their sustainability goals.

2. Asset performance management

Asset performance management (APM) includes data capture, integration, visualization and analytics to improve the reliability and availability of physical assets.

AI/ML models for APM can help:

  • Extend the useable life of an asset by proactively detecting and fixing improper asset operating parameters (such as fans changing speed too frequently).
  • Predict when an asset needs maintenance based on operating conditions. They enable movement from scheduled to predictive maintenance to lower costs and reduce unplanned outages.
  • Learn normal operating conditions, such as energy usage, for assets and then identify anomalous operating conditions by monitoring real-time data streams.

Poorly functioning assets can be a huge drain on power. They can also cause system failures and user dissatisfaction if not identified quickly. We’ve developed models that detect operating anomalies to help us improve asset lifespan, reduce costs and lower energy consumption. These optimizations also help improve customer satisfaction by contributing to industry-leading reliability (99.9999% average uptime) in our data center operations.

3. Capacity management and planning

As business requirements evolve, many companies have limited resources to address changing compute, storage or networking capacity needs. Over-provisioning or under-provisioning data center capacity can lead to greater waste and increased costs. AI/ML technologies can help efficiently manage and plan the resources need (such as space, power and cooling) to maximize revenue, reduce TCO, and operate more sustainably.

AI/ML models provide valuable data to:

  • Learn space layout and optimize the useable space in a data center, while maintaining constraints around temperature and humidity.
  • Plan from current and past data center power usage to optimize near-term and predict future power consumption.

As mentioned above, we’re using AI/ML models to assess our current and future capacity and power needs to better meet our customers’ changing business requirements. By proactively anticipating our customers’ colocation and digital infrastructure needs, we can be ready for them wherever they want to do business.

4. Customer relationship management

According to the Equinix 2023 Global Tech Trends Survey (GTTS) report, 79% of IT leaders plan to apply AI to improve customer experience. Specifically, AI/ML models can help:

  • Balance the supply and demand, capacity and operating constraints of your data center to better serve employees and customers.
  • Identify dissatisfied customers so your support and sales organization can proactively work to retain them.
  • Recommend customer connection opportunities, within and across data centers, to access critical services in a secure and low-latency manner.

With AI/ML, you’ll gain greater insights into how to align your solutions to customer demand. We’ve developed intelligent models that are helping our marketing teams identify customer requirements. By doing this, we can see repeatable customer interconnection use cases and more readily offer solutions that best meet their needs.

5. Security

Cyber-attacks, security breaches and data leaks are top threats to business success. Digital leaders are looking to AI/ML technologies to improve their data center security posture by closing gaps in their security controls.

Protect your equipment and data using AI/ML models for capturing:

  • Video analytics to monitor and flag suspicious activities in and around your data center and customer cages.
  • Cage/cabinet access data to model and detect anomalous access patterns.

Data center security requires both physical and virtual controls to cover an ever-expanding attack surface. In addition to our advanced security equipment, techniques and procedures, AI/ML models analyze video streams to detect anomalies, making our data centers and our customers’ equipment and data even more secure.

6. Productivity improvements

Improving data center workflows and processes using AI/ML will increase user satisfaction and productivity, and ultimately give you valuable insights for optimizations that future-proof your data center.

AI/ML models can help:

  • Improve productivity by using Generative AI to provide responses for submission to compliance questionnaire tools and retrieve answers in a Q&A format from internal knowledge base systems and playbooks.
  • Facilitate incident management by clustering events in similar topics to solve new incidents quicker based on past learnings and best practices.
  • Reduce data center work ticket processing time by parsing details of customer work and sending them to the right team quicker.

Eliminating manual processes and improving productivity through AI/ML techniques can help you respond to customers’ needs quicker. At Equinix, we’ve removed manual approvals for the majority of our transactions through workflow automation, expediting our business processes and our customer response time. We’re also using natural language processing (NLP) in our intelligent automated ticket routing as a proof of concept in our Equinix Smart Hands® remote data center management. By quickly classifying support requests via NLP, we can improve customer experience with faster responses.

These are just a few examples of how AI and ML are currently being applied at Equinix, with more to come in the future.

To unlock the full power of AI technology in your own organization without sacrificing flexibility or control over your data, you may need to adopt a private AI strategy. This means avoiding publicly available AI services and instead building your own models, hosting them on private infrastructure, and training them using your proprietary datasets.

Visit the Equinix Indicator to learn more about how digital infrastructure can enable private AI. You’ll hear from industry experts about:

  • How to build an AI-ready data architecture
  • How to interconnect the components of your distributed AI infrastructure
  • How to build sustainable and responsible practices into your AI deployments


[1] Omdena, “The Ultimate MLOps Guide: How to Master MLOps for ML Engineers and Data Scientists,” Lukasz Murawski, Lead Machine Learning Engineer at Equinix, April 22, 2022.

Divesh Kumar Director, Product Software Architecture and Engineering
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