What are the guidelines for artificial intelligence data storage?
Enterprises choosing the wrong artificial intelligence storage platform may have a serious impact. Therefore, people need to understand 6 criteria that may affect the enterprise's choice of artificial intelligence data storage strategies.
Artificial intelligence and machine learning have now become the two most important tools for companies, which can help companies use their core digital assets to create competitive advantages. But before adopting artificial intelligence data storage, companies must consider a series of requirements based on how machine learning platforms acquire, process, and retain data.
First check the life cycle of the data used by machine learning software, as this can help companies understand what to consider when choosing storage for artificial intelligence. Initially, companies must obtain data to train machine learning or artificial intelligence algorithms. These are software tools that process data to learn tasks, such as identifying objects, processing videos, and tracking motion. Data can be generated from various sources and is usually unstructured in nature, such as objects and files.
The training process will acquire data assets and use machine learning or artificial intelligence software to create algorithms for processing future data sources. When training or developing algorithms, artificial intelligence software will process the source data to develop models that can create insights or meet business needs.
Developing machine learning algorithms is rarely a single process. As companies accumulate more and more data, their algorithms will be improved and improved. This means that very little data will be discarded, but will grow and reprocess over time.
Standards for artificial intelligence data storage
Before choosing storage for an artificial intelligence platform, companies must first consider the following:
Developing machine learning and artificial intelligence algorithms requires high-performance storage and high-performance computing. Many artificial intelligence systems are based on GPUs (such as Nvidia DGX), which can reduce the burden of many complex mathematical calculations involved in developing accurate algorithms.
Public cloud service providers have begun to provide GPU-accelerated virtual instances that can be used for machine learning. Running machine learning tools in the public cloud can reduce the investment cost of building infrastructure for machine learning development, while providing the ability to extend the infrastructure needed to develop machine learning models.
The challenge in using public cloud computing is how to import data into the public cloud in a cost-effective and practical way. Object storage based on cloud computing is too slow to meet the I / O requirements of machine learning; therefore, local block storage must be used. Delays in mobile data and delays in machine learning mean increased costs for operating infrastructure.
Another problem with public clouds is the cost of data export. Although cloud computing service providers do not charge fees for moving data into their platforms, they charge fees for any data accessed from public networks external to their platforms. As a result, although public clouds provide computing flexibility, it is not always easy to move data in and out of cloud platforms in a timely and cost-effective manner.
Cloud computing vendors are developing storage products that can run their products in a public cloud that spans on-premises infrastructure and cloud platforms. These products can effectively copy data or move data to the cloud platform, and only move the results back after completion. These replication technologies have high bandwidth efficiency, making it feasible to deploy storage data on-premises and import it into cloud platforms for analysis.
Machine learning and artificial intelligence storage need to be isolated from computing. Building artificial intelligence data storage can be difficult because storage networks and adjusting storage must consider other factors to work with machine learning applications.
The pre-packaged products enable cloud computing vendors to test and optimize their products before delivering them to customers. Today, there are some storage products that combine popular artificial intelligence software, CPU and GPU and other computing, network and storage devices to provide a platform that supports artificial intelligence. Before deploying these systems, many detailed adjustments have been completed. Although cost may be an issue, for many customers, pre-packaged systems can reduce barriers to adopting artificial intelligence storage.
Obviously, choosing the right artificial intelligence data storage platform is a trade-off indicator, such as performance, scalability, and cost. Setting up the storage platform correctly is critical because the amount of data involved is very large. Choosing the wrong product can be a costly mistake. As with any storage product decision, it is also important for companies to talk to cloud computing vendors to understand exactly how their products meet the needs of artificial intelligence and machine learning. Its participation process should include demonstrations and evaluations as a prelude to any possible purchase decision.
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