Data centers play a central role in global business operations, with most companies worldwide having one to two operational data centers. However, a common challenge faced by these businesses is a surplus of data without the ability to use it effectively for actionable decisions. AI platforms have the potential to solve this by efficiently analyzing all types of data, including video and sensor data, and providing valuable insights. This can give businesses a significant advantage, especially if they have accumulated a large amount of data to feed into such systems. To harness the power of AI, businesses should first determine which AI function suits their needs. There are two broad categories to consider: machine learning (ML) and deep learning (DL). It is crucial to understand the differences between these processes and the types of data they interact with when choosing the right fit for a business. ML is well-suited for processing large workloads, making it effective for training decision trees. The accuracy of ML models improves with more data used for the baseline. However, these workloads require a scalable storage platform that can handle the growing demand for data storage as more data is collected and sampled. DL, on the other hand, aims to mimic human thought processes and represents the world through a hierarchy of concepts. Unlike ML, DL relies on neural networks instead of decision trees.

Training DL models requires GPU-powered systems to provide the necessary computing power, and these systems benefit from as much data as possible to be effective. They also require a scalable system that can handle ongoing growth. Similar to ML, DL platforms rely more on high input/output workloads than high throughput. Understanding the desired end-goal of data application and storage is crucial to ensure proper processing with the corresponding workload methodology. Operators should also consider the storage solution they want to implement and take preventative measures to avoid system failures. Choosing the right AI function is just the beginning; integration is another aspect to consider. Constructing AI data storage can be challenging, and pre-packaged storage products that combine popular AI software, CPUs, GPUs, networking, and storage can be a tempting solution. However, these pre-packaged solutions come at a significant cost. Engaging with vendors, requesting demonstrations, and evaluations are important steps to ensure that the chosen AI function and storage platform align with business requirements. In conclusion, selecting the most suitable AI data storage platform involves considering factors such as cost, scalability, and performance. Given the volume of data involved, making the right decision is crucial for organizations. Engaging with vendors, understanding product capabilities, and requesting demonstrations are essential for well-informed purchasing decisions.