How To

Deploying Open-Source AI Right Out of the Box


Artificial intelligence (AI) and machine learning (ML) are generating astounding results all across the IT industry, but it is impossible to ignore the reality of how complex it can be to implement these solutions across the industry. Open-source AI software can be an existing, ready-to-use resource for organizations looking to leverage the power of AI/ML without custom-building an in-house solution. The result is a productive use of open-source software to deploy AI right out of the box.

Key Takeaways:

  • Open-source software has the benefit of countless meaningful contributions from the general public, which are particularly invaluable when it comes to machine learning.
  • Not only is open-source AI built on myriad data sources, but it also allows organizations to bypass the costly hurdle of building a custom AI model.
  • The first step toward implementing this type of open-source AI is to adopt an AI-ready infrastructure that can accommodate the unique needs of large language models now and into the future.

What is the ideal open-source software to deploy AI?

Open-source software carries with it a license that allows anyone to use, alter, or distribute the software and its source code for any purpose. This creates a dynamic in which multiple parties, or even the public at large, can collaborate in developing and improving the technology.

An open-source philosophy can be especially fruitful when it comes to AI because as the software becomes exposed to more data and more use cases, it becomes more robust and generally useful. When organizations use open-source software to deploy AI, they are leveraging deep learning that is only possible through hundreds of thousands or even millions of hours of practical use time.

The leading open-source deep learning and MLOps frameworks include large language models (LLMs) such as Llama2, Falcon, and MPT. Open-source software that uses these open-source frameworks as a foundation is capable of generating useful insights with little or no additional training from the end user.

Using the Nutanix platform as a solution for deploying AI can simplify the learning curve that so many organizations experience when attempting to launch an AI-ready stack. Nutanix is an active contributor to the AI/ML open-source software community and brings that expertise into developing its own deployment solutions.

Anatomy of Nutanix GPT-in-a-Box

Nutanix Cloud Platform for AI (GPT-in-a-Box) is a full-stack software-defined AI-ready platform, along with services to help organizations size and configure hardware and software infrastructure suitable to deploy a curated set of large language models. This technology includes open-source software to deploy AI workloads, including PyTorch framework & KubeFlow MLOps platform.

Why choose an open-source AI solution?

According to Kevin Korte, president of Univention North America, AI needs to continue to become a community affair. “Do you want to extend your customer service? Train the AI using recorded interactions. Want to strengthen cybersecurity? Use spam, scam, and phishing emails as your training sets. Thanks to open-source, it's all doable without the costs of developing the base software and language model.”

Fortunately, many business leaders are aware of the benefits of using open-source software to deploy AI and are opting for this route. In fact, the results of the Nutanix State of Enterprise AI Report show that only 10% of surveyed organizations plan to build their own AI models.

Industry sentiment toward leveraging existing AI models or building custom solutions

Lee Caswell, SVP of Product and Solutions Marketing at Nutanix, expands on why so many decision-makers are turning away from custom-built AI models. “It may in part be due to the prevailing skills gap many enterprises are experiencing when recruiting AI talent. It might also be driven by a need to invest wisely and efficiently in AI. By leveraging existing, pre-trained large language models—which organizations can then adapt to their own needs by fine-tuning with proprietary data—companies can accelerate their AI strategies and avoid the huge investment that comes with training LLMs.”

What are the first steps toward deploying open-source AI?

Using open-source software to deploy AI is all about simplifying the process of implementing AI/ML, lowering costs, and accelerating AI-driven success so organizations can immediately generate meaningful returns on their investments. The first step toward reaping those benefits is to further reduce deployment and operational complexity with an AI-ready platform offering a full stack.

Nutanix AI solutions jumpstart any AI transformation with optimal infrastructure that delivers control, privacy, and security to maximize AI success. It is an ideal platform that streamlines the transition to AI model-driven operations, simplifies the way users access apps and data, and lowers TCO overall.

Best of all, Nutanix empowers teams to deploy open-source AI right out of the box. A GPT-in-a-Box is part of the Nutanix Cloud Platform for AI and includes the capability to use open-source software to deploy AI using curated sets of LLMs and leading AI frameworks.

The Best Platform for Using Open-Source Software To Deploy AI

Leaning into open-source software removes many of the most challenging roadblocks along the way to implementing game-changing AI solutions, but organizations still need an AI-ready platform that enables them to deploy usable AI models right out of the box. With that in mind, the Nutanix platform is the best place to launch a future-proof AI initiative.

This is due largely to the commitment to open-source software at Nutanix. Nutanix openly documents its own architecture and actively contributes code within a variety of communities, not the least of which is the open-source AI/ML community.

Using open-source software to deploy AI means relying on technology that is constantly changing and evolving for the better. Stay on top of the industry’s growth by checking out our State of Enterprise AI Report and keeping up with the Nutanix Blog.

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