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Making Open-Source AI Easy To Learn

Artificial intelligence is boosting productivity and fueling cost-cutting measures in every aspect of the IT industry. However, many organizations are encountering roadblocks during their AI transformations in the form of steep investment requirements for educating their teams on AI and machine learning.

Companies looking to stay competitive need a solution that makes it easy to learn how to use open-source software to deploy AI.

Key Takeaways:

Using open-source software cuts out the middleman when it comes to building and training robust large-language AI models.
Open-source solutions make it easy enough to learn how to leverage AI that companies can start generating AI-driven results the very next day.
Nutanix provides a simple solution for open-source AI with a full stack available as an in-the-box package.

Why Use Open-Source Software To Deploy AI?

Open-source software is a unique concept in which the rights to use, modify, and further distribute software are all available to anyone and for any purpose they choose. This benefits individuals and organizations who gain easy access to innovative software. Moreover, the collaborative nature of open-source software also benefits the development of the programs themselves by exposing them to many talented developers and numerous sources of data.

These benefits are invaluable in the improvement of large language AI models. Using open-source software to deploy AI means that an organization’s AI deployment can tap into the vast amount of data available through the open-source community. This is data that a model needs in order to become robust and adaptable, but that is not easily or cost-effectively available anywhere else.

Implementing Nutanix architecture for artificial intelligence is one way for companies to ensure they have the means to process bigger data sets that come with the use of open-source software. There is a clear need for agile, scalable architecture built to accommodate the demands of any software developer or data scientist.

Findings from Grand View Research suggest that the global open-source services market will expand at a compound annual growth rate of 16.9% between 2023 and 2030, while the global artificial intelligence market will grow at an even more astounding CAGR of 37.3% during the same period. Both of these technologies comprise an inevitable step forward in the future, particularly when used effectively in tandem.

How Can Organizations Start Using Open-Source AI Today?

Using open-source software to deploy AI already removes much of the complexity and a large portion of the learning process from AI adoption. With that in mind, it becomes exceedingly simple to begin leveraging the capabilities of open-source AI today with the right tools and platform.

The Nutanix State of Enterprise AI Report reveals the top AI trends according to responses from key IT decision-makers from around the world. Out of these respondents, 90% asserted their plans to use existing AI models so as to see immediate results, and 41% specifically intend to leverage open-source models to build AI applications.

One way for organizations to achieve their goal of quickly adopting an existing AI model is with a GPT-in-a-Box solution. Nutanix GPT-in-a-Box provides a fully-trained model that is usable today with very little learning curve, due largely to the open-source AI and MLOps frameworks built into Nutanix Cloud Platform.

Is There a Simple Solution for Open-Source AI?

Nutanix provides an ideal AI platform that seamlessly runs apps, data, and AI from core to edge. In addition to the built-in GPT-in-a-Box solution, the Nutanix platform also makes it easy to use open-source software to deploy AI by streamlining the transformation. Its features allow organizations to independently buy, build, or modify AI models from any source and run them anywhere with push-button simplicity.

Implementing open-source solutions is a seamless process with Nutanix. This is because Nutanix demonstrates a commitment to contributing to the open-source software community. Our history with open-source projects translates to a platform design philosophy that spurs AI/ML innovation.

The result is an AI-ready stack that simplifies the AI learning curve. It is a simple software-defined solution that can fine-tune the user’s chosen GPT models and supports a curated set of large language models.

Not only does Nutanix bring simplicity to the otherwise complex and resource-intensive endeavor that is AI-powered development, but it does so in a way that delivers enterprise-grade edge computing. Being on the cutting edge of open-source software to deploy AI should not mean compromising on data security, costs, scalability, or reliability.

Nutanix Is the Best Place To Use Open-Source Software To Deploy AI

The biggest hurdle in using AI to generate business success is the actual process of building and training AI models in the first place. Open-source solutions cut out the most strenuous aspects of AI transformation, but even they are not without their own in-depth learning requirements.

Nutanix offers the best platform to harness open-source AI because it provides a true turnkey solution that includes everything needed for AI-ready infrastructure. IT teams can build, fine-tune, and run GPT or LLM models using their current personnel and skill sets.

When complex AI/ML concepts become so easy to implement and learn, it becomes possible to use open-source software to deploy AI on a company-wide scale without breaking the budget and with minimal pain points along the way.

Contact us for more information regarding our open-source initiatives and AI/ML platform training resources.

Learn more about the role of hybrid multicloud technology in the GenAI era.

“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.”