Object storage is an essential component of AI applications. It is the most suitable and only adequate solution to help solve AI/ML’s model-training challenge. Here are some reasons why object storage is important for AI applications:
Huge amounts of data necessitate huge amounts of storage, and AI/ML workloads require a solution that can infinitely scale as the data grows. Object storage is the only storage type that can scale limitlessly to tens of petabytes and beyond within a single, global namespace. Being able to scale elastically and seamlessly based on demand, by deploying new nodes non-disruptively, whenever and wherever needed, is a great advantage.
Regularly backing up a multi-petabyte training data set is not only cost and time prohibitive, it’s downright unrealistic. Most object storage systems, by design, do not require backups. Rather, they store data with sufficient redundancy so it’s always protected.
The format of object storage allows for easily storing and managing a high volume of unstructured data, which is becoming increasingly important with artificial intelligence, machine learning and big data analytics.
Storage architecture influences the ability to gather metadata. Whereas file and block systems do not enable application or user-defined extended attributes, object storage systems offer unique ways to identify data with incredibly rich, customizable metadata.
In the journey to unlock AI's boundless potential, object storage stands tall as a foundational pillar. Its prowess in scalability, data protection, unstructured data management, and metadata enrichment charts a compelling path towards AI excellence. Embrace the future with object storage - where innovation knows no limits.
Make the migration, its worth it.