How to adopt the standard of artificial intelligence data storage?

Last Update Time: 2021-05-14 10:30:59

Before choosing storage for an artificial intelligence platform, companies must first consider the following:

(1) Fees. The price of artificial intelligence data storage is a key factor for companies to consider buying. Obviously, corporate management and those involved in purchasing decisions want data storage to be as cost-effective as possible, and in many cases this will affect the company ’s product choices and strategies.

(2) Scalability. Enterprises need to collect, store, and process large amounts of data to create machine learning or artificial intelligence models. Machine learning


image.png

The source data is required to grow exponentially to achieve a linear increase in accuracy. Creating reliable and accurate machine learning models may require hundreds of terabytes or even petabytes of data, and this will increase over time.

Building a petabyte storage system usually means using object storage or scale-out file systems. Modern object storage can solve the capacity requirements of artificial intelligence workloads, but they may not meet other conditions, such as high performance. Scale-out file systems can provide high performance and good scalability, but storing the entire dataset on a single platform can be expensive. Due to scalability requirements and the cost of large-capacity products, block storage is usually not the right choice for machine learning or artificial intelligence. The only exception is in the public cloud.

Changes in storage costs have introduced the idea of tiering or using multiple types of storage to store data. For example, object storage is a good target for storing large amounts of inactive artificial intelligence data. When data needs to be processed, it can be moved to a high-performance file storage cluster or object storage node designed for high performance, and once the processing is completed, the data can be moved back.

(3) Performance. The storage performance of artificial intelligence data includes three aspects. First, and perhaps most importantly, delay. This defines the processing speed of each I / O request issued by the software. Low latency is important because improving latency directly affects the time required to create machine learning or artificial intelligence models. Complex model development may take weeks or months to run. By shortening the development cycle, companies can create and improve models faster. When checking the delay function, due to the fluid nature of object access, the object stores the reference time to the first byte instead of the delay of a single I / O request.

Another aspect of performance is throughput, and the speed at which data can be written to or read from the storage platform. System throughput is important because artificial intelligence training processes a large number of data sets, and it is common to repeatedly read and re-read the same data to accurately develop models. Sources of machine learning and artificial intelligence data (such as sensors on autonomous vehicles) can generate terabytes of new data every day. All this information must be added to the existing data store with minimal impact on any existing processing.

Setting up the storage platform correctly is crucial because the amount of data involved is very large.

The final aspect of performance is parallel access. Machine learning and artificial intelligence algorithms process data in parallel and run multiple tasks that can read the same data multiple times and can span many parallel tasks. Object storage is good at parallel read I / O processing because there are no object locks or attributes to manage. The file server keeps track of open I / O requests or file handles in memory. Therefore, the number of active I / O requests depends on the available memory on the platform.

Machine learning data can contain a large number of small files. In this regard, the file server can provide better performance than object storage. A key question posed to artificial intelligence storage vendors is how the performance characteristics of their products will change between large and small file types.

Since most large object storage is too large to be backed up regularly, reliable erasure coding has become a basic function of artificial intelligence storage platforms.

 

If you want to know more, our website has product specifications for artificial intelligence data, you can go to ALLICDATA ELECTRONICS LIMITED to get more information