When we imagine AI, we usually think of robotics, self-driving cars, and maybe even smart cities. But we don’t often spend enough time considering the infrastructure that powers these technologies. Without a robust and scalable infrastructure, AI applications cannot thrive or function.
What do we mean when we refer to AI infrastructure? It’s the technology stack required to run the machine-learning algorithms that power AI applications. This technology also gives the parties, whether machines or staff members access to the computing resources necessary to test, train, and deploy AI applications.
Designing the infrastructure that best fits your AI needs requires analyzing the main tech stack components and how they match up with the requirements of your application(s). You want your solution to be scalable but also optimized to your current needs. Consider a few key areas that are essential to AI.
AI Depends on the Processing of Vast Amounts of Data
If there is one component that is inseparable from functioning AI applications, it’s data. However, not every business’s data needs are comparable. For example, some organizations need to process data on the fly, while others can leverage post-processing capabilities, potentially processing raw data on the cloud.
For the majority of advanced applications, latency will be an issue, and it’s important to consider fog computing hardware that can provide processing at your business locations. Lowered latency will ensure that data doesn’t lose its value by the time it’s processed, and it will enhance your team’s AI capabilities.
Storage types are also essential since this will help you balance the right latency and density requirements with the cost. Additionally, storage will play a critical role in forecasting future upgrades as you have to scale your operation to keep up with AI growth. By considering the amount of data your AI applications will produce, it’s possible to predict your future data needs.
AI Applications Need to Communicate with the Network
Your network capabilities allow AI applications to communicate with edge devices, fog compute servers, and even the cloud. This communication may even happen on a near-constant basis. Hence, robust networking hardware is key to uninterrupted production.
It’s also important to consider future growth. As your AI learns and algorithms become more complex, your data transfer demands will rise. If you didn’t anticipate this growth at the start, you might find yourself running into bandwidth bottlenecks, which will slow down or, in some cases, impede progress altogether.
The nature of AI infrastructure means that it often leverages computing power from several sources connected over the network. This distributed setup can present security challenges, especially when businesses are operating on a global scale. Leveraging infrastructure management tools can help teams keep a pulse on network metrics and react automatically to mitigate risk. An example of this is intent-based networks that respond to security threats in real-time and protect your infrastructure.
AI Applications Require Immense Processing Power
AI applications and neural networks are compute-intensive and require hardware that can handle extensive multitasking. The computing needs of AI exceed what traditional CPUs can handle on their own. This is because CPUs are designed for sequential processing, which is quickly overloaded with tasks that require server multitasking.
Servers optimized for AI applications often leverage GPUs since this type of processor is more capable than a CPU at handling parallel processing. These GPU-focused servers allow AI models to train much faster and are key to providing sufficient processing for advanced AI loads.
Other Non-Hardware Related Considerations
As we’ve mentioned, data is crucial to high-quality AI applications. It’s important to establish strong company-wide policies to ensure you’re handling your data in the best way. This includes data governance rules that ensure data is available to those who need it but also protected from falling into the wrong hands.
Additionally, it’s important to establish consistent data scrubbing practices to weed out the chaff and guarantee that algorithms are only fed relevant information. When you input the highest quality data, you’ll get the best results.
Finally, since people are vital to any business, you must think about your AI training program. You can have the best hardware, but if your team isn’t properly trained, it’s all for nothing. Recruitment and training, both initially and ongoing, are indispensable for creating strong AI teams and keeping them that way.
AI Infrastructure Is Complex, but You Don’t Have to Design Your Setup Alone
Data storage, networking, and processing power all play a pivotal role in your AI application’s success. So how can you ensure your infrastructure is ready for prime time? It’s important to remember that components must be optimized to work together since mismatched hardware will waste resources. To ensure AI success, identify your AI needs and optimize your hardware to match.
Our team at Intequus specializes in hardware to support AI applications. We can help you build custom solutions that meet your needs today and can scale for tomorrow. Talk to one of our experts to learn more about custom hardware for AI infrastructure.