If you were to break down AI’s parts into a recipe, data would be the most crucial ingredient by far. That’s to say that without data, AI is not possible. And AI doesn’t need just a touch of data; some programs require petabytes of data and are always hungering for more. The importance of data in AI means that the storage solutions organizations use are critical to performance.

Carefully chosen storage solutions can reduce costs, help organizations scale, and provide stellar performance regardless of location. Another factor impacting AI is that businesses are moving towards a hybrid cloud setup to improve IT infrastructure flexibility and cost control. What do IT teams need to consider to make strategic AI storage choices?

5 Requirements for AI Storage

It’s worth mentioning one more time that AI and its subsets, machine learning and deep learning, live on data. Lots of data. For this reason, systems must be scalable, cost-effective, protective of data, location agnostic, and built for hybrid storage. Let’s consider what each of these implies for your storage choices.

1. Scalability. AI requires large data sets, and storage solutions need to be able to scale accordingly. For example, the Inception V3 model from Google contains a bit less than 24 million parameters and requires about 1.2 million data points (in that case, labeled images) to be trained for classifying images. Without scalable storage solutions, managing data sets at this scale would be impossible. IT teams must leverage modular storage options and flexible storage structures like object-based storage to untether your scalability.

2. Cost efficiency. When AI infrastructure exceeds your IT budget, it’s no longer a feasible solution. For this reason, teams need storage that’s not only scalable but cost-effective. Custom storage solutions allow you to build hardware with job-specific components and leave out unnecessary optimizations.

3. Data protection. AI infrastructure needs to be set up in a way that it protects itself from data loss. Storage systems should be designed with redundancies built-in. Since you may be dealing with data at the petabyte level, these systems must back up data in the most efficient way possible. This may mean a hybrid setup with a combination of extremely dense storage and high-performance SSDs that help you optimize cost and performance.

4. Location agnostic. Teams that work with AI and DL will frequently leverage a combination of on-premise equipment and cloud infrastructure. Performance, cost, and regulatory issues make hybrid computing essential to getting work done. Your on-premise equipment must be scalable and cost-effective if your team will be able to use it effectively. Additionally, on-premise hardware should easily integrate with the cloud and simplify data flow to/from the cloud.

5. Hybrid storage. Data sets have differing performance requirements, and storage solutions must be flexible enough to meet the needs of your AI. Instead of a one-size-fits-all approach, teams do their best to customize solutions with hybrid architectures that help them reach project goals. Flexible systems that can mix storage drive types allow teams to optimize storage for the job without missing a beat.

Match AI Storage Solutions to Your Business Needs

AI empowers businesses to make data-driven decisions, automate business processes, forecast more effectively, and more. Don’t avoid AI because hardware makes it seem inaccessible. Intequus helps companies build the custom storage solutions they need to run their AI and scale with ease. If you’d like to learn more about how custom solutions can help you make your investment go further, talk to our team today.

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