In the AI era, your data prospers from a ‘clean room’

Silicon manufacturing is a delicate operation that requires precision and accuracy. In fact, the tiny features that are so carefully etched into chips also make them susceptible to microscopic particles. Those particles can compromise the entire fabrication process.

This is why clean rooms — spaces that use filtration systems to limit airborne particles — are so vital to ensuring consistency in manufacturing processes. In this regard, semiconductors share a lot in common with data. Just as chip manufacturing requires pristine, carefully calibrated processes, data must be cleaned, prepped, and staged before it can achieve repeatable results and a high yield. 

Moreover, just as chips fuel data throughout computing systems, high-quality data feeds generative AI models that create content for improving business outcomes. 

Architecture tailored for GenAI

A traditional computing architecture won’t suffice in powering GenAI systems. Instead, data that fuels GenAI requires special approaches and supporting technologies.

These systems take vast amounts of data tokens (the letters, words, and phrases GenAI models are trained on), then process and refine them to deliver actionable intelligence via applications.  

“The traditional data center architecture is ill-equipped for these generative AI workloads,” said Dell COO Jeff Clarke at Dell Technologies World. “A whole new computing architecture has emerged.”

Technology, Clarke said, is transforming from a computation-driven workflow to a workflow powered by context and reasoning. 

Organizations require systems equipped with GPUs for massive parallel processing, high-speed storage, and high-speed networking, as well as intelligent data pipelines, to feed the freshest datasets to GenAI models. 

While the models may execute vector math behind the scenes, the resulting applications are where the real magic is on display for end users.

These include digital assistants and AI agents that turbocharge sales operations, accelerate the creation of marketing collateral, and conduct predictive maintenance in manufacturing facilities. 

Such capabilities have executives keen on GenAI’s potential to transform organizations. According to Boston Consulting Group, 85% of C-suite executives said they planned to increase investments in GenAI and AI technologies this year.

Partner up for smoother AI operations

High-quality data, along with high-speed and reliable infrastructure, are essential in ensuring that GenAI initiatives help organizations drive productivity and innovation. 

But cultivating clean data isn’t easy, and there are only so many data engineers with the chops to create quality data and feed it to the algorithms that populate models. These experts must also augment, fine-tune, and refine the models to ensure they are producing accurate results. 

Moreover, connecting data pipelines to the right equipment and training and augmenting GenAI models isn’t for the faint of heart. Neither is keeping up the steady evolution of models, which are getting smaller and more powerful while running on laptops and smartphones.  

And while it’s true that data and applications have grown increasingly decentralized, many organizations will elect to run their GenAI models in their data centers or at the edge. Bringing AI to the data affords organizations efficiency, security, and control. It also requires a new way of thinking about system architecture.

This is where partners can help. It’s also why Dell Technologies is building the Dell AI Factory. 

The Dell AI Factory uses modular, open infrastructure to give organizations the flexibility to adapt as they seek to achieve their desired business outcomes, whether this includes digital assistants, agents facilitating autonomous tasks, or something else entirely. 

The modular approach, along with the growing, open ecosystem, helps organizations keep up with the latest and greatest AI innovations — including smaller models. Meanwhile, Dell’s professional services organization can help organizations prepare and synthesize their data and identify and execute use cases, increasing the speed of deployment and time to first token. 

To make the most of the AI boom, businesses must produce repeatable results as they work to create content at scale. 

Click here to learn more about the Dell AI Factory.

This post was created by Dell with Insider Studios.