Artificial Intelligence and Machine Learning have become critical to enterprises across all industries. However, AI and ML applications produce massive quantities of data in real-time, saddling organizations with petabytes that must be managed properly to extract value. Storage is sometimes overlooked as enterprises think about how they can best support AI and ML, but using the right storage infrastructure is essential to effectively leveraging these applications. Here are seven reasons why object storage is the best choice for AI and ML.
1. Limitless Scalability
Artificial Intelligence and Machine Learning systems need to process vast amounts of data in a short timeframe—an essential attribute because large data sets are required to deliver accurate results. This data volume drives significant storage demands. Microsoft, for example, required five years of continuous speech data to teach computers to talk. Tesla is teaching cars to drive with 1.3 billion miles of driving data. Managing these data sets requires a storage system that can scale without limits.
Object storage is the only storage type that scales limitlessly within a single namespace. Object storage makes it possible to scale out horizontally – adding new nodes whenever needed – while file and block storage architectures generally use cumbersome vertical scaling. Object storage’s modular design is what allows the capacity to be added at any time. Organizations can scale elastically with demand, rather than ahead of demand.
2. Cost Efficiency
A useful storage system must be both scalable and affordable, two attributes that don’t always co-exist in enterprise storage. Historically, highly scalable systems have been more expensive on a cost per capacity basis.
Large AI data sets are not feasible if they break the storage budget. Object storage is built on low-cost hardware. Combine that with low management overhead and space-saving data compression features, and the result is up to 70% less cost than traditional enterprise disk storage.
3. Rich Metadata
Detailed metadata is critical for Artificial Intelligence and Machine Learning – it makes it possible to easily search for, locate, and analyze data by Data Scientists in AI and ML apps.
File and block storage only supports small amounts of metadata (i.e., the date created, where it was created and who created it). But object storage supports fully customizable metadata. As a result, it’s much easier to organize, find, and use data, strengthening the accuracy of AI and ML models.
For AI and ML data sets that grow without limits, a parallel-access architecture is essential. Otherwise, the system will develop choke points that limit growth.
Object storage employs a shared-nothing cluster architecture, which means that all parts of the system work in parallel. Data throughput grows continuously as the system scales.
Backing up a multi-petabyte training data set is not always feasible, as it would often be cost and time prohibitive. But it can’t be left unprotected, either. Instead, the storage system needs to be self-protecting. Object storage is designed with redundancy built-in, so data is protected without requiring a separate backup process.
Furthermore, it allows users to select the level of data protection needed for each data type to optimize efficiency. Systems can be configured to tolerate multiple node failures or even the loss of an entire geo-distributed data center.
While some AI and ML data will reside in the Cloud, much of it will remain in the data center for a variety of reasons: performance, cost, and regulatory compliance are three of them. Object storage provides the scalability and economics of Public Cloud storage on-premises, with better performance and greater control (as well as lower TCO when Public Cloud data access charged are factored in).
Regardless of where data resides, integration with the Public Cloud will still be an important requirement for two reasons. First, much of the AI and ML innovation is occurring in the Cloud. On-prem systems that are Cloud-integrated will provide the greatest flexibility to leverage Cloud-native tools. Second, there’s likely to be a fluid flow of data to and from the Public Cloud as information is generated and analyzed. An on-prem storage solution should simplify that flow, not limit it.
Object storage is the most cloud-integrated of all storage architectures. First, object storage typically employs the S3 API, the de facto language of cloud storage. Second, object storage can tier to Amazon, Google, and Microsoft Public Clouds, with the best object storage solutions enabling users to view local and cloud-based data within a single namespace. Third, data stored on the Cloud from object storage is directly accessible by Cloud-based applications. This bi-modal access lets enterprises employ both Cloud and On-prem resources interchangeably.
Storage is a critical part of the infrastructure that supports Artificial Intelligence and Machine Learning. With massive volumes of data produced by AI and ML applications in real-time, it’s essential that organizations use a storage platform that can keep pace. Object storage is the only storage architecture that provides the scalability, cost-efficiency, Cloud integration, and other key capabilities needed to fully support AI and ML.