Storage Tips for Artificial Intelligence and Machine Learning

Data storage plays a critical role in the areas of Artificial Intelligence (AI) and Machine Learning (ML). The volumes of stored data are indeed extremely important, and must be accessible quickly. No AI or ML project can be fast, stable and above all efficient without a data processing that is itself fast and efficient. This is why planning for optimal storage plays a particularly important role here. Different points must be taken into account.

special planning. This article reminds you of things to consider and what to expect.

Data storage plays a critical role in the areas of Artificial Intelligence (AI) and Machine Learning (ML). The volumes of stored data are indeed extremely important, and must be accessible quickly. No AI or ML project can be fast, stable and above all efficient without a data processing that is itself fast and efficient. This is why planning for optimal storage plays a particularly important role here. Different points must be taken into account.

The 14 key definitions of Storage

Storage issues have probably become the most important of the IS, understanding the issues is essential: this definition guide provides you with a solid expert base to move forward!

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In the context of AI and ML, the training data must be stored so that it can be written quickly, even in large quantities, and read just as quickly.

Consider price and scalability

Many organizations store their data in the cloud when it comes to efficiently managing AI or ML projects, especially for scalability. Storage spaces in the cloud, such as those in Microsoft Azure or Amazon AWS, offer fast, easy, and almost unlimited scalability. Companies managing their own storage solutions must ensure that they also enable the local storage medium to evolve quickly and at a reasonable cost. This element should be considered when planning the storage solution. Even if a data carrier meets the requirements of the moment, it can change very quickly when the AI ​​or the ML comes to require more space. It must then be possible to add some

Artificial intelligence solutions process large amounts of data in a short time. The corresponding storage system must therefore offer unlimited scalability so that an AI and ML system can be efficiently exploited. Modular storage media are the ideal solution here.

Obviously, the price also plays a vital role. Indeed, an unlimited storage volume costs most of the time extremely expensive. As a result, it can happen that some AI or ML systems can not be operated profitably because the associated storage medium is too expensive.

The administration of the storage solution must also be able to be planned, and should not increase too much as the amount of data increases.

Use modern storage functions – consider hyperconverged networks and hybrid approaches

Ideally, the storage solution should also meet modern requirements to avoid relying on obsolete technologies or investing in a stalemate. Software layered storage solutions and hyperconverged infrastructures are the main technologies to take into account when setting up data carriers. Similarly, appliances are often wise because they can also be used in other areas, including for AI and ML solutions.

Many companies are reluctant to rely on cloud storage or cloud features. Yet, especially in the field of AI and ML, cloud technologies are almost inevitable. That’s why the chosen storage solution should allow hybrid approaches, and work both in the local computing center and in connection with the cloud. By supporting hybrid technologies, organizations are gaining tremendous flexibility and scalability.

Data security and stability

The security of the storage media must be guaranteed at all times. The large amounts of data stored by the AI ​​and ML can only be saved in rare cases. Indeed, backing up such amounts of data naturally implies a space that is just as consistent on the backup media. This storage space is expensive and often unavailable, many companies give up saving these data. In addition, the time required to back up such huge data volumes is often impossible to predict in the backup windows. But the storage solution used for these projects must also be secured against failures, and especially stable enough to avoid as much data loss as possible. The storage system should, as far as possible,

Ensure compatibility with different protocols and allow parallel access

A storage solution for IA and ML environments must be able to communicate with multiple protocols. It is therefore important to ensure that the server is compatible with all access modes used in the enterprise. In addition, the support must be sufficiently powerful and stable to allow multiple access in parallel to these large amounts of data, which is often the case. There must be no loss of connection when different sectors of the medium are used in parallel.

Storage dedicated to AI, ML and DL must be scalable, fast and stable, and offer maximum compatibility. The storage medium must support both the cloud connection and the local operation in the enterprise computing center. The ability to access data with different protocols must be guaranteed. The storage space must of course be sufficient, but at a fairly advantageous price so that the storage costs do not exceed the benefits of the solution of IA, ML and DL. The data rate must be sufficient to allow multiple users and services to access it in parallel.

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