10 hours ago
Everyone's Talking About AI Compute—But It All Starts With Storage
Dave Friend is the cofounder and CEO of Wasabi.
When you think of AI, there are probably many things that come to mind, such as how to use it, where it's headed and what powers it. The conversation typically centers around compute—A.K.A. all the CPUs and GPUs you hear about when discussing AI. While compute is critical, there is a significant aspect of AI that is often overlooked: data. Although it may not be widely discussed, the reality is that these massive, unstructured and ever-growing data sets are what are truly driving global AI growth.
As AI models become larger and more sophisticated, accessing the necessary data to train them is becoming a significant challenge for users. This is due to multiple factors, including the ever-increasing amount of data needed to train AI models. To make matters worse, hyperscaler storage that many rely on is expensive, overly complex and not optimized for the accessibility and performance that AI workflows demand. Additionally, enterprise data used to train AI systems is becoming a favored target for malicious actors. All of these factors combine to make AI adoption incredibly challenging, expensive and time-consuming.
The reality is that most companies aren't struggling with AI compute limitations. They're hitting walls because they can't store, access and manage the data quickly, securely and affordably enough to support real-time inference, fine-tuning or long-term retention.
If AI needs to run efficiently and cost-effectively, so does the data it learns from. To address these growing problems and fully leverage the benefits AI has to offer, organizations should implement a scalable cloud storage solution that provides cost-effectiveness, security and hybrid capabilities.
Best Practices And What Leaders Should Expect
However, not all data storage providers are created equal. The cloud giants that dominate the industry charge exorbitant fees to access data, making it more difficult, expensive and time-consuming for users. This makes training AI and storing the data that AI gleans a costly and challenging undertaking. To address this, organizations should seek out affordable cloud storage providers that don't charge these fees. This will enable them to easily access their data in a way that makes AI training as seamless and cost-effective as these storage buckets can be easily scaled up and down depending on need. This is ideal for training AI, as the storage will need to hold both the data required to train AI models and store the resulting information. Being able to scale up easily and down will ensure that an organization is adequately prepared for AI models and can adjust as needed.
Just as important as where you store the data is ensuring it is stored securely. Cyberattackers are increasingly going after enterprise data due to its vital role in AI operations. As a result, it is crucial for IT leaders to ensure that the storage solutions they choose are adequately protecting their data. When selecting a provider, organizations should remember to look for one that offers robust data protection offerings that ensure the storage sets are impenetrable. Organizations should also take notice that the data is hidden from bad actors in the event of a breach to prevent deletion or ransomware threats. These are critical for avoiding an attack and protecting critical enterprise data.
Key Features And Approaches To Security
An essential part of a secure data management program is immutable backups, which prevent a malicious actor from modifying or deleting the stored data. Immutable backups are an air-gapped solution that isolates data from potential threats such as ransomware or accidental deletion. IT leaders should consider immutable backups to ensure their data is impenetrable and cannot be encrypted or deleted. Additionally, no secure cloud management program would be complete without employee training on cyber protection. By regularly updating cybersecurity best practices for employees and providing training, organizations can effectively prevent malicious actors from breaching their networks and accessing critical data.
An approach that combines cost effectiveness and security is hybrid storage, which involves storing data in different methods and locations. This can include one copy in the cloud, one on-premises, and one on a hard drive. Incorporating cost-effective solutions like the cloud reduces expenses, while having the data in multiple locations allows it to be readily available in case of a cyberattack. For AI training, data can be readily available in the cloud, but it can also be stored on-premises for added security.
While it is easy to get caught up in the AI boom, organizations must take their time incorporating the emerging feature. Technology decision-makers should ensure they prioritize cost-effective and secure ways to store the data necessary for proper AI training. Without it, they may be left behind their competitors in the AI adoption race.
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