How to Support Developers in Building AI Workloads 

Over the next decade, developers, engineers, data analysts and a host of other roles will shape the future of AI. The goal of these efforts is to elevate human lives through innovative and visionary applications of AI. But to get there, the AI architects of today need robust data platforms that can support their ideas for tomorrow.

The required tools to support this kind of revolutionary AI need to be transformative. Like the mass-market Apple Macintosh 128K that launched 40 years ago, the data platforms that will enable these applications need to represent a quantum leap ahead of anything previously available.

So what will they look like? And what possibilities might they unlock for those who, until now, have struggled to handle AI workloads with subpar data platforms?

There are four main components to a complete, real-time data platform that can keep pace with the constant metamorphosis of generative AI.

1. In real time: AI workloads require an astronomical amount of fresh data. A truly real-time platform needs to be capable of zero ETL (extract, transform, load) across all analytical and transactional systems and agnostic to the source or destination of data. In the context of generative AI, this includes real-time extraction of query workloads, including query text, execution time, owner details and database details. As large language models (LLMs) proliferate and consume inputs from a more diverse set of end users than ever, this kind of real-time speed and analytics is a must.
2. For any data: The development of AI applications doesn’t always involve semi-structured data like JSON and BSON, or completely structured text that fits neatly into a spreadsheet. While developers need tools that can handle and process this kind of data in fractions of seconds, the data platforms we’re talking about need to be just as powerful with less traditional data sets. The next generation of AI needs to intake and parse unstructured data like video, audio, PDFs and images. Next-generation platforms and tools also need to handle the full life cycle of data — from creation to integration, from visualization to insight.
3. For any application: While generative AI is top of mind now, data platforms have to support a broad spectrum of applications. This includes transactional applications, petabyte-scale analytics, front-office applications and more. The future calls for compatibility — integrations are key, and so are relationships with major technology partners such as Google, Microsoft and IBM that can help support the data platform.
4. For everyone: Part of the beauty of AI’s promise is that its transformation isn’t driven exclusively by developers and engineers. Data scientists, machine learning specialists, prompt engineers and a variety of other roles can all contribute to building AI applications and products. And because AI is being shaped by organizations of all sizes, data platforms can’t be cost-prohibitive. Startups and developers need access to compute power and integration services that won’t stymie progress due to their cost.

Like the Macintosh mouse, the kind of data platform we’re describing represents an evolutionary leap forward compared to what’s previously been available on the market. With the above four components in mind, we’ve developed and launched SingleStore Pro Max, which can holistically meet the demands of the generative AI boom and whatever the next revolutionary step will be.

Through available free trials, we’re on a mission to bring the power to handle massive AI workloads to as many visionaries as possible. To learn more about the dozens of new features and services available in the SingleStore Pro Max platform, dive deeper or reach out for a demo.