The data used for the training of Machine Learning and Artificial Intelligence algorithms require storage systems tailored to their specific needs. Find out how to choose the best AI data storage system.
Data is essential for artificial intelligence, and can be considered the “fuel” for AI systems. Indeed, before a task can be performed, a neural network must be trained from data. For example, an AI designed to recognize a cat in a photo will have to be trained from a multitude of photos of cats.
In fact, the data used for artificial intelligence… require specific storage systems. These systems need to be adapted to the way in which data is acquired, processed and stored by Machine Learning platforms. Here are the criteria to consider when choosing a storage system for data on an AI platform.
In order to be able to generate precise and high-performance algorithms, artificial intelligence systems have to process huge volumes of training data…. For example, Microsoft had to use five years of speech data to teach computers to talk. Similarly, Tesla trained its autopilot system on data generated from 1.3 billion miles travelled.
The system chosen to store this data must therefore be capable of handling such volumes. In addition, over time, Machine Learning algorithms require an ever-increasing amount of data to continue to improve accuracy. It is therefore essential to a “scalable” storage system whose capacity can be adapted to the exponential needs of AI.
As a general rule, we therefore opt for object-oriented storage systems, since this is the only type of storage that can be extended indefinitely within a single namespace. In addition, the modular design of these systems allows for easy and instantaneous addition of capacity.
However, one can also turn to scale-out systems that will offer scalability and high performance. The only drawback of this type of system is that storing complete datasets can be very expensive. As an alternative, one can now opt for the Public Cloud .
Cost is also a key consideration when choosing a storage system for AI and Machine Learning. Clearly, a company’s priority is to achieve the following store the data needed for its artificial intelligence projects without exploding its budget.
It is therefore important to choose the most economical solution, while ensuring that it meets other specific needs related to the storage of data for artificial intelligence.
AI data storage must also be efficient. First of all, the latency should be as low as possible so that requests issued by the software can be processed quickly. The higher the latency, the longer it will take to create IA / ML models.
In addition, consideration should be given to the time needed to write and read data. This is an important criterion, as the data will usually have to be read and rewritten several times by artificial intelligence systems.
Finally, the storage system should ideally allow parallel access. For good reason, the IA and Machine Leraning algorithms process the data in parallel. This means that they perform multiple tasks that can read the same data multiple times between multiple parallel tasks. Again, object-oriented storage is optimal. However, for large amounts of small files, file servers can be more efficient.
Training a Machine Learning algorithm with data can take several weeks. In all that time, it is imperative that the storage system is functional and continuously available. Even updates must be able to be carried out without downtime.
Besides.., the storage system must allow for rapid recovery in case of failure of a server, node or media such as SSD or HDD. For good reason, such incidents are unavoidable in a large system.
Knowing that the backup of a data set of several petabytes is impossible, it is important that the storage system be able to protect itself. For example, some systems use the wipe code technique to distribute data between multiple nodes to minimize the impact of a possible failure.
Even though it is an on-site system, it is important that an AI storage system offers integration with the Public Cloud. This will allow you to take advantage of the latest innovations in the field of AI, but also to transfer data to or from the Cloud.
Cloud integration also makes it possible to benefit from increased storage and computing performance. Indeed, many Cloud service providers now offer GPU-accelerated virtual instances that can be used for the development of Machine Learning algorithms. Running Machine Learning tools in the cloud also reduces the cost of developing a dedicated infrastructure, while providing the scalability that is needed.
However, at the present time, the Cloud alone is not suitable for AI data storage. Object-oriented systems based on the Cloud are too slow and therefore too expensive. It is therefore important to use on-site block storage as well.
In general, even if part of the data is stored on the Cloud, the rest will remain on site for performance, cost or compliance reasons. In fact, on-premise storage should offer the same cost and scalability as cloud storage.
In conclusion, when choosing the best AI data storage platform, it is essential to consider several factors such as performance, scalability and cost and find the best trade-off. Take care to contact the vendors directly to check whether their products match your business needs. If you need, do not hesitate to ask for a demonstration.