Nowadays, generative artificial intelligence (AI) is all anyone can discuss. From producing professionally written cover letters for jobs to easy-to-use code snippets, generative AI models can boost productivity in many ways. The generative AI market is estimated to cross $91 billion by 2032.
While AI has been present in the tech industry for a while, generative AI is making a significant impact for the first time. For those unfamiliar, generative AI is the type of AI that’s capable of producing a variety of content, including text, images, code, video, and even synthetic data. Open AI’s ChatGPT app is generative AI powered by machine learning.
Since the introduction of the much more powerful GPT4 model by OpenAI and the broader adoption of generative AI models (including Microsoft’s Bing or Snapchat’s MyAI), many industries are benefiting from this technology. So, can data centers also use this type of AI in their day-to-day operations?
In this article, we will provide the following:
- An exploration of how tools like ChatGPT can be leveraged inside a data center.
- An outline of how to choose and work with generative AI platforms.
- A look into how generative AI impacts data centers in terms of handling applications.
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Can Generative AI Be Helpful With Data Center Management?
AI-powered chat tools like ChatGPT aren’t just for students to write essays or marketers to create copy for social media. Generative AI can be leveraged in different ways to improve specific processes, especially those manual, time-consuming ones.
Data centers that typically rely on powerful tools and experienced engineers can benefit from generative AI apps. While we’re just beginning to explore the use cases of such apps, here are some data center-specific uses:
Quick and Easy Data Analysis
Data centers constantly need to optimize their network and infrastructure to ensure they maintain a committed level of service. To make improvements and detect problems early on, data centers rely on analytical tools that use the available data to provide insights.
While generative AI may not entirely replace analytical tools, it can complement them in many ways. For instance, it can assist managers and engineers in coming up with better forecasting models for predicting traffic peaks.
Its analytical capabilities can also fill the gaps in the data center’s existing analytical toolset.
Troubleshooting Issues
Instead of spending time reading equipment manuals or looking for answers on developer forums, engineers configuring and maintaining data center equipment can enlist the help of generative AI to troubleshoot issues.
As these models have been trained on massive amounts of data online and in books, they can provide succinct, simplified answers to queries in seconds. It’s a quick way to troubleshoot technical issues with equipment or software tools that comprise the data center infrastructure.
The admin or engineer trying to troubleshoot any issue can prompt the chatbot and quickly receive possible solutions. It can also serve as a quick second opinion when unsure about the problem.
Generating Code
The most common use of tools like ChatGPT in the tech industry is code generation. Generative AI can write code in various languages, from the commonly used ones like JavaScript and Python to more niche ones like Rust.
You can provide a prompt explaining what the code needs to do, its purpose, and what language it should follow. It may take a couple of prompts to get the code that works. Still, it’s a great way to accelerate code generation.
There’s not as much coding in managing data center infrastructure. Still, the code-generating capability of generative AI can also help set up and configure new or existing devices.
Reporting on Performance and Incidents
Data centers can use generative AI to create reports on performance and incidents. While the data for reporting will primarily come from network management software and other tools, generative AI can put the findings into easy-to-read and understandable text for managers and executives.
It can interpret complex findings, such as metrics and trends, simplify them, and coherently present them.
Working With Public Generative AI Platforms
Applications like ChatGPT help increase productivity, but their use on an enterprise level can be risky. Enterprise data centers subject to data privacy regulations may not want to use a public generative AI platform. Platforms like ChatGPT, which are open to the public, may see the company using data to improve its service.
Does that mean enterprises and their data centers can’t use ChatGPT? A viable solution is to use the foundational model that powers ChatGPT with a purpose-built API. Many platforms have already taken this approach to enhance the model's functionality and tweaked it to suit their specific use cases.
An even better approach is to create a customized foundational model by training it on data you own. Such a model may be even more helpful for data center-specific tasks, such as detecting bottlenecks or optimizing network pathways.
Using a public AI platform is cheaper than building one from scratch. However, the availability of the basic model makes it cost-effective to create a customized generative AI tool that’s secure enough to be used within your on-premise data facilities.
A free public platform may provide a different level of security than a paid enterprise solution may offer. For data centers, it’s critical to use third-party solutions that use advanced security features in line with the enterprise’s overall security policy.
Generative AI and the Impact on Data Centers
In addition to using generative AI, data centers must also prepare to facilitate its use by other enterprises. As the adoption of AI increases, it will become an essential strategy for most IT businesses. That, in turn, means data centers will have to adapt to support generative AI workloads.
Remember that AI workloads are much different from traditional applications today’s data centers are familiar with. In other words, many enterprises may have to overhaul their infrastructure to support generative and predictive AI applications.
Extensive computing power is necessary for both phases of AI usage, for example, training models and then using them in different applications. This extra computing also requires power and cooling.
Change is occurring too fast, and data centers must catch up in capable hardware and onboard expertise. In other words, enterprises with on-premise data centers will inevitably need to refresh parts of their infrastructure to support AI workloads better. Similarly, companies must upskill their IT teams to manage modern, AI-ready data centers.
While some experts are optimistic about the new doors AI will open for industries worldwide, others are sounding the alarm on infrastructure not being ready to handle the massive wave of AI use cases.
Seize the Opportunity
As AI gets used in unprecedented ways, data centers can facilitate generative AI applications and use them to improve operations. Enterprises with data centers can create generative AI apps on top of existing models to support and streamline day-to-day management tasks.
Besides incorporating AI in their own operations, data centers must also prepare to support the AI workloads on their servers. Hardware readiness is critical to making the most of AI.
Ready to seize the opportunity? Learn how PivIT can help you procure next-gen AI-ready servers to take your data center to the next level.
