Five Storage Requirements for AI and Deep Learning

Five Storage Requirements for AI and Deep Learning

Artificial intelligence and deep learning are highly dependent on data sets. The more data the system uses, the more accurate the result it provides. That is why businesses are also using data to make the processable enough for high performance. Here, an important fact is, users need an efficient storage solution for AI and Deep Learning.

Storage Requirements for AI/DL

AI/DL models have some specific storage requirements. Here are five of them:

1.      Scalability and Durability

AI systems require a great deal of data to be taught. All this data needs a short time to process. Extensive data sets increase the accuracy of the AI system. For example, famous car brand Tesla is qualifying cars to auto-drive. For this, it requires 1.3 billion miles of data. To store the vast amount of data, businesses need a large-scale or without-limit storage system. This system also needs to be auto-protected to ensure durability.

2.      Cost-Effective

To store significant AI and Deep Learning datasets, companies need affordable storage systems. Generally, highly scalable storage systems are costlier than normal ones. As scalability is also important, users need to find one provider to ensure feasibility and scalability at the lowest price. In addition, features like space-saving data compression can highly reduce the cost.

3.      Hybrid Storage Solution

Data can be of different types. According to the type, the system requires different performance levels, hardware, scale, etc. Therefore, to get the best results, users must choose the best storage system that reflects just the right combination of technologies. Using a hybrid architecture can help to perform better while optimizing the costs.

4.      Integration

Developers prefer cloud platforms for AI/DL innovation. However, users also need continuous but smooth data flow to analyze and generate accurate insights. That is why integrating data with the cloud is an inseparable part of AI and the deep learning process.

5.      Parallel Access

Most AI/DL models use the split system and divide the tasks into multiple parallels. Thus it gets easier to get access to the same file from multiple servers. This parallel access process results in high throughput. This model needs a storage system that can cope with the changing demand without invading the performance quality.

Fulfilling all these requirements ensures high performance. As AI/DL uses unstructured data, businesses need to find the best storage provider that can help to meet the requirements.