September 28, 2023
3 Keys to Success with a Generative AI Platform
To capitalize on artificial intelligence, organizations must avoid common pitfalls associated with choosing infrastructure to support the technology.
There are useful — sometimes revolutionary — applications in nearly every industry, and leaders in every enterprise are thinking about how to implement generative AI within their companies. And yet, many businesses are struggling to effectively build out their AI initiatives. One key hurdle is infrastructure: Too often, IT organizations attempt to build AI on the same infrastructure that supports more mainstream enterprise workloads.
What many don’t realize is that generative AI places unique demands on IT resources, and having an infrastructure that’s not optimized for AI model development can end up stifling data science innovation and delaying time to market. To avoid these pitfalls, IT and business leaders should consider three key factors.
1. Developer Inefficiency Is Costing You
To make the most of their time, AI developers and data scientists need a user experience that’s as simple as pushing a button. These users don’t need to know about infrastructure; instead, they need an elegant user interface that will allow them to maximize the efficiency of their model development workflow without having to worry about the infrastructure underneath.
These aren’t just users who need to be kept happy; they’re in-demand, high-priced professionals. When they’re waiting on resources, the business is essentially burning cash — and also falling behind in the race to roll out transformative AI solutions before its competitors do. A dollar spent on AI infrastructure might actually be costing you three, if that infrastructure has your developers idling or expending effort that adds no value, such as re-engineering one’s software stack to make that infrastructure usable.
2. Infrastructure Is Not Enough
When provisioning AI resources, many IT leaders instinctively turn to Infrastructure as a Service, looking to set up bare-metal servers in the cloud for the lowest possible price per GPU-hour. This is understandable, given the way organizations have become accustomed to provisioning resources for more traditional enterprise workloads. However, when it comes to AI, it often makes more sense to move up the stack and adopt a higher-value platform that allows organizations to focus on delivering a productive experience for data scientists and developers, rather than managing compute instances. This includes developer workflow tools that streamline model prototyping, as well as accelerated data science libraries, optimized AI frameworks and even pre-trained models that offer a jump-start to innovative apps.
Without a full-stack platform for AI development, many businesses discover too late that a dollar invested in Infrastructure as a Service doesn’t always deliver a dollar of data science output.
3. Your Team Needs Access to AI Expertise
AI talent can be extremely expensive, and that’s assuming you can find it. For some organizations, AI talent is essentially unavailable at any price. With enterprise AI being such a nascent space riddled with unsupported, unproven technology, today’s businesses need enterprise-grade 24/7 support and access to AI-fluent practitioners who know how to solve problems.
This was an important consideration as NVIDIA developed the DGX™ platform, and it’s why NVIDIA makes its AI expertise available, on-demand, to its customers — helping them achieve better results, quicker. This expertise can range from addressing problems related to optimizing models for faster training runs to finding the root of software incompatibilities that cause training jobs to crash. A full-stack platform that comes with access to AI expertise can help ensure applications are delivered to market quickly and cost-effectively.
Story by Tony Paikeday, who is a Senior Director of AI systems at NVIDIA, responsible for go-to-market for NVIDIA’s DGX platform. In his role, Tony helps enterprise organizations infuse their businesses with the power of AI via infrastructure solutions that enable insights from data. Tony was previously with VMware, where he was responsible for bringing desktop and application virtualization solutions to market, as well as key enabling technologies, including GPU virtualization and software-defined data center.