How To

Getting the Most out of AI-Ready Infrastructure

Artificial intelligence (AI) is making waves in the IT industry, with tools like ChatGPT and countless other recent innovations all producing ripples that reach every business and organization.

If you are among the many enterprise leaders looking to get ahead of the curve by implementing AI and machine learning (ML) solutions at your company, then you are already taking the first steps toward harnessing AI-ready infrastructure. Getting the most out of that investment, on the other hand, may require more than simple buying power.

Key Takeaways:

Infrastructure designed with AI in mind puts you in the position to automate processes and generate insights right away.
The cloud is the foundation for much of today’s IT infrastructure, so your AI capabilities need to extend to the cloud and between multiple cloud locations.
With AI safety being a primary concern for many would-be AI adopters, the ability of your cloud platform to adapt security and privacy in the AI age is crucial.

What is AI-ready infrastructure?

AI-ready infrastructure is a complete datacenter stack designed to maximize the productivity of GPUs running AI workloads at any scale. Building your own AI-ready stack or implementing a third-party solution means that your infrastructure can handle the deployment of AI models and applications without disruption.

The promising benefits of an AI transformation include streamlining a much-needed upgrade of traditional IT infrastructure and simplifying operations across the board. Of course, this transformation is not without its noteworthy challenges — namely, there are lingering concerns across the industry regarding the security and reliability of AI.

Sentiments regarding promises and challenges of AI transformation

The right AI solutions will simplify your learning curve during the transformation phase so that you can address any concerns from a place of knowledge and decisiveness. The Nutanix AI-ready stack provides this simplicity by its nature as a complete turnkey solution that provides seamless scaling of virtualized compute, storage, and networking components with minimal burden on the user.

Nutanix Cloud Platform for AI (NAI; also called GPT-in-a-Box) perfectly exemplifies the concept of AI-ready infrastructure, especially for organizations already operating in the Nutanix cloud environment. However, getting the most out of this type of infrastructure solution requires knowing how to apply and adapt it to a wide variety of situations.

Extending to the AI-ready cloud

AI-equipped infrastructure built with hyperconvergence (HCI) can extend seamlessly to the public cloud and beyond, giving an organization the power to leverage the best machine learning innovations both on-premises and at the edge. This hybrid cloud approach grants the flexibility needed to keep sensitive data and important training information secure in the private cloud while also providing the vast computational power needed for model training that is only available in the public cloud.

The main appeal of the cloud, though, is its capability to store vast amounts of data and computational resources. Extending AI-ready infrastructure to the cloud enables you to get the most out of your new investment by allowing ML processes and language models to automatically tap into those vast reservoirs of data when generating actionable insights.

Cloud-assisted AI efficiency becomes possible wherever AI meets the hybrid cloud. Expensive graphics processing units (GPUs) are ideal for powering AI/ML processes, but procuring entire racks of GPUs can be excessively costly for individual organizations. Capable cloud providers that are able and willing to bear the burden of maintaining this hardware are the ones who are leading the charge when it comes to preparing IT infrastructure for future AI innovations.

Among those innovations is the ability to extend not only to the AI-ready cloud but also to the AI-ready edge. ML-powered processes are at their best when they have high-quality data from which to draw insights, so it only makes sense to place those processes close to the edge of the network where the most valuable data sources also lie.

Adapting security to the AI Age

Security is an ever-present concern for IT, but in the AI age, there are new concerns regarding whether artificial intelligence will misuse or expose data or even endanger the organization when it calls the shots. According to the Nutanix State of Enterprise AI Report, 53% of organizations view data security as a key driver of AI application and infrastructure upgrades.

Key drivers of AI application and infrastructure upgrades, with data security as the leading driver

Despite these concerns, the responsible use of AI-ready infrastructure is contributing greatly to protecting the IT industry against cybercrime. It is important to keep in mind that when AI is used correctly, human decision-makers will always have the final say and the ability to halt or reverse automated processes.

The cloud itself, where most or all modern AI processes take place, is a major part of what enforces data security as it pertains to AI/ML due largely to the cloud security practices already in place on major platforms. Security measures from a provider like Nutanix include centralization, intrusion detection, encryption, and zero trust ideology.

Allied Market Research reports that the global cloud security market size was valued at USD 35.8 billion in 2022 with a projected increase to USD 125.8 billion by 2032. This expected growth, alongside the rapid evolution of AI/ML technology, clearly shows that cloud security remains something of the utmost importance that can and will adapt to changing times.

Get the most with a platform equipped with AI-ready infrastructure

Artificial intelligence powered by groundbreaking machine learning technology is becoming a more worthwhile investment with each innovation, especially as these solutions gain greater capability to extend to the cloud and facilitate security. However, getting the most out of an AI investment means having the technical support and functional resources you need when you need them and without unnecessary complications or roadblocks.

Nutanix provides exactly that with a comprehensive cloud infrastructure that includes a pre-trained AI solution as part of the environment. The capabilities of GPT-in-a-Box include planning, design, and deployment services, all while ensuring that you maintain control over your data and applications.

Implementing AI-ready infrastructure does not have to be complicated, and in fact, the solution that puts the power of AI/ML in your hands with the greatest degree of simplicity is the one that will likely yield the most efficiency.

Learn more about enterprise data protection and the benefits of DevOps as AI/ML changes the IT landscape.

“The Nutanix “how-to” info blog series is intended to educate and inform Nutanix users and anyone looking to expand their knowledge of cloud infrastructure and related topics. This series focuses on key topics, issues, and technologies around enterprise cloud, cloud security, infrastructure migration, virtualization, Kubernetes, etc. For information on specific Nutanix products and features, visit here.”