In recent years, object storage has emerged as one of the leading storage solutions, not just for general use like cloud storage, but also for artificial intelligence (AI) systems. Its unique characteristics make it well suited for handling large volumes of unstructured data that AI systems often rely on. However, while object storage offers many advantages for AI, it also presents some challenges that need to be addressed.

Automating Scale

One of the main reasons object storage is a good choice for AI is its ability to quickly scale. AI systems often require vast amounts of data when training neural networks, and object storage can be automated to easily scale by adding additional storage nodes without having to do this process manually. When dealing with AI applications where data volumes can be unpredictable and grow rapidly, object storage’s automated scalability can come in handy.

Fast Data Access

Another advantage object storage presents for AI applications is its ability to handle unstructured data. AI systems often need to process diverse data types, such as images, videos, or text. Because object storage comes with descriptive metadata tags, accessing and processing data becomes easier and less cumbersome for AI apps. This can help speed up an application’s computing power.

Data Integrity

Data stored in object storage systems is typically replicated across multiple nodes and regions, which can help ensure that data remains accessible even in the event of hardware failures. This durability is crucial for AI applications, where data integrity is paramount. Built-in fault tolerance is standard practice with object storage providing greater resilience.

Some Challenges for Object Storage and AI

Despite these advantages, object storage also poses some challenges for AI applications. One challenge is latency. Object storage systems are designed for high-capacity storage rather than low-latency access. AI applications that require real-time processing or low-latency access to data may find object storage less suitable.

Another challenge is the complexity of managing object storage systems. Unlike traditional file systems, which organize data into hierarchical structures, object storage systems store data as objects with unique identifiers. This can make it more challenging to manage and access data, especially for developers who are not familiar with object storage management.

Object Storage is Here to Stay

It’s safe to say that object storage has already become one of the predominant types of storage used in general storage as well as for AI applications, and this trend will likely continue for the foreseeable future. Its scalability, ability to handle unstructured data, and durability make it an attractive option for AI systems. However, challenges such as latency and complexity need to be addressed to ensure that object storage meets the needs of AI applications if this is the route a developer takes for their app. When considering what type of storage to use for an application, it’s best to always make a specific list of use cases and different scenarios at the beginning of the app-development planning process to ensure an application is paired with the best type of data storage.