Artificial Intelligence – What’s Holding Back Your AI Strategy?
According to some surveys approximately four out of every five AI projects don’t reach production. That amounts to quite a few companies struggling to make it happen. It’s no wonder that many businesses and IT leaders are hesitant to invest in AI at scale.
Yet simply increasing infrastructure investment does not guarantee AI success: it may even bring more challenges. Instead, IT leaders need to consider making the right investment, and that requires a focus on building systems that are optimized for AI, rather than trying to use general-purpose infrastructure technologies.
Fortunately, the story of AI’s global growth continues to accelerate. Given that one out of every five AI projects IS reaching production, there is still reason for hope. Let’s look at the most common problems that AI projects face and determine what it takes to be a success.
The following are five common challenges with AI and how you can address them:
01. Slow AI Implementation
By the nature of its computing, your AI environment will test your IT infrastructure in a never-before-seen way. AI workloads have specialized compute, network, and storage needs. And if these requirements aren’t addressed at the outset of your project, performance limits will occur.
The challenge is that these restrictions aren’t readily apparent in the beginning. At the first test, your system may appear to function perfectly. And when problems are encountered, your infrastructure team’s first impulse may be to add capacity, which comes at a cost. Instead, what’s needed is not simply more storage capacity or computing power but a different approach to optimizing systems for AI workloads.
02. Your system is drowning in data
Data, whether it’s images, video, or language processing, is the foundation of AI systems: model training and learning needs a LOT of data, and real-time inference systems need to consume live data streams at very high rates. This deluge of information can easily overwhelm traditional IT infrastructure.
On the other hand, working with an insufficient amount of data may mean that AI systems can fail to produce the valuable insights that were promised. To prove its worth, an AI architecture needs to be designed with a data-first strategy from the very beginning. Using AI-optimized systems, network and storage architecture often means that organizations can see from 10x to 50x more throughput, compared to regular IT technologies.
03. Systems are not Optimized for AI Workloads
Yes, smaller fast-processing systems are easy enough to build. However, the parallel GPUs needed to process AI data at scale need much more data than a traditional system can handle. On the other hand, if the storage or networking is a bottleneck, then you are unable to feed data fast enough to keep your AI-optimized GPU fully utilized.
Therefore, the entire system, processor, network, and storage, must be planned like a superhighway to handle petascale volumes of data. Otherwise, your AI systems won’t be able to generate and process deep learning algorithms. And that means your organization won’t realize the game-changing insights promised from AI.
04. Scaling Issues
An AI system hasn’t truly been tested until it’s been scaled to production levels. It’s easy to underestimate the need for streamlined data flow within large-scale AI systems. In fact, it’s common for large-scale systems to be more complex – and so latent performance problems and bottlenecks can appear as they scale up.
So, in order to support the handling of exponentially large datasets, AI environments need to be built from the ground up with scaling in mind. This applies not only to the system itself but supporting operational functions like backup and recovery. A well-functioning AI system needs to be scalable across all stages of your AI workflow, from ingesting to archiving.
05. Shadow AI Projects
A common reaction to challenges seen in one AI project can be the rise of rogue AI projects elsewhere within an organization. These ‘hidden’ AI projects can result in redundancy, repetition and hidden costs. And, of course, your team has no way to support them when they don’t know they exist. However well-intentioned, shadow AI projects only serve to exacerbate the problems of a decentralized AI strategy, and in the end, nobody wins.
To Learn More About AI-Optimized Infrastructure
Ultimately, AI has too much potential to ignore, and needs a long-term investment. The best approach is to have an executive-led AI strategy – an AI Center of Excellence that serves as a strategic resource for the entire organization.
As an IT leader, your key to successful AI projects is a data-first approach, and to put a plan for scalability at the top of your list. And when you partner with DDN, you can start with a turnkey, easy-to-implement, scalable storage architecture that unleashes the power of your AI infrastructure. Contact one of our storage experts today to put your AI project on the path to success!
DDN is the world’s largest private data storage company and the leading provider of intelligent technology and infrastructure solutions for Enterprise At Scale, AI and analytics, HPC, government and academia customers. Through its DDN and Tintri divisions the company delivers AI, Data Management software and hardware solutions, and unified analytics frameworks to solve complex business challenges for data-intensive, global organizations. DDN provides its enterprise customers with the most flexible, efficient and reliable data storage solutions for on-premises and multi-cloud environments at any scale. Over the last two decades, DDN has established itself as the data management provider of choice for over 11,000 enterprises, government, and public-sector customers, including many of the world’s leading financial services firms, life science organizations, manufacturing and energy companies, research facilities, and web and cloud service providers.