The unstoppable growth of AI data and the challenge for infrastructure
Over the last couple of years, Artificial Intelligence (AI) has moved from an emergent technology to a transformative force. With the adoption of AI increasing, the infrastructure supporting it needs to adopt as well. Organizations adopting AI find themselves at the brink of yet another data explosion. What was once thought of as “big data” is now common practice as enterprises scale up from terabyte-scale storage to petabyte- and even exabyte-scale. At the AI Data Infrastructure Field Day (Tech Field Day) MinIO presented their vision of Exabyte scale demand in the Data Industry and why their object storage is the only true storage solution to cope with these gigantic amounts of data.
The challenge organizations face is beyond just “more data.” AI environments generate, process, and store enormous volumes of unstructured data in myriad formats, including video, audio, and endless streams of log files and sensor readings. The underlying storage architecture must efficiently scale while addressing the difficulty of AI workloads in distributed, complex hybrid environments. The big problem with classical storage solutions becomes clear, performance bottlenecks and skyrocketing costs. For large, data driven, organizations, legacy storage systems cannot keep pace with the relentless demands of modern AI applications.
This is why Object Storage becomes the backbone of AI data infrastructure. Purpose-built to handle vast datasets, object storage is delivering the scalability, cost-efficiency, and control needed. It will meet the demands set by enabling distributed data workflows and maintaining high performance across large datasets for AI-driven organizations. Why is object storage uniquely suited to take on the demands of the AI era and why has it become a key component in the data infrastructure of the most progressive companies?
Why Object Storage is Integral to AI Data Infrastructure.
Artificial intelligence workloads require enormous volumes of data in various forms, ranging from logs and sensor-generated files all the way up to audio and video. Object storage has the capability for managing huge volumes of unstructured data. Its uniqueness makes it a very well-suited option for AI data infrastructure since it can manage volumes measured in petabytes or exabytes, unlike conventional storage.
Object storage supports distributed, flexible architecture that shares affinity with the hybrid environments of most businesses today. The design of object storage can have consistent access and control across multiple environments as data migrates between a multitude of cloud providers and on-premises systems. Containerization and orchestration technologies are innately supported by object storage, making AI pipeline integration seamlessly easy, and lets organizations take advantage of the cloud operating model across public and private clouds.
In other words, object storage provides the scalability and flexibility necessary to manage the data flows on which AI thrives.
Legacy Storage versus Object Storage: Meeting Modern Scale and Speed
Legacy storage solutions, such as Network File Systems, cannot scale to hundreds of petabytes without hitting significant performance bottlenecks. At the scales AI demands, traditional storage solutions are plagued by latency, reduced throughput, and escalating costs associated with hardware scaling.
Object storage was designed for modern data workloads and can deliver high throughput at scale while remaining economically viable. Object storage flips the conversation to throughput, not raw IOPS, which is a primary consideration in AI workloads. Even though the AI models require access to huge volumes of data for training, these individual IOPS often become secondary to making sure data moves well at very high speeds, a trait of object storage.
Another strong benefit is that it will treat a large data object as it will treat a small one, hence it would be fit for different types of data in size. From huge video files to small telemetry data, object storage will scale in ways legacy solutions simply can’t, and for that reason, it has become the go-to choice for most AI environments.
The Economics of AI Data Storage and Cloud Repatriation
Cost is proving to be one of the key factors for an organization when creating its AI infrastructure. Though most of them have started with public cloud solutions, the exponential operational costs have compelled more-and-more of them to repatriate onto private and/or hybrid clouds. Since most companies use AI projects as strategic investments that drive growth and competition, the first order of business for organizations is managing the cost associated with storing data. Object storage presents a more sustainable economic model in AI environments.
Object storage in either private or hybrid cloud looks like a way to cut costs when compared to the public cloud for storage. This is because software-defined storage on commodity hardware allows an enterprise to be in control of infrastructure costs and avoid being locked into proprietary vendors. Consequently, they have leeway to make flexible procurements or deployments. Also, object storage supports scaling for business needs, whereby companies would grow their storage only when workloads are increasing, instead of investing at the onset in very expensive, fixed infrastructure.
For AI leaders looking to manage a tighter budget, Object Storage provides the right balance among scalability, performance, and cost efficiency, qualities that make it the storage choice for a lot of the newly created AI data infrastructures.
Security and Control in AI Data Infrastructure
Data is a strategic asset, and with companies building their AI infrastructure, the control and security of sensitive data becomes the highest priority. With object storage, there are a lot of features in terms of security that traditional storage does not provide, making it a very impactful solution for the security of AI data environments.
Object storage offers object-level encryption, which extends encryption at a very granular level for your data. While various other forms of storage would offer encryption at the volume or file system levels, object-level encryption extends that to provide greater levels of control such that security management can be done at the exact pieces of data. This is important in AI, where several datasets might contain pieces of information that have different degrees of sensitivity.
In addition, object storage natively supports data immutability and ensures non-modification or deletion of data-a defense against ransomware and unauthorized changes. This way, one can safely train and store AI models, knowing their data is secure and tamper-proof, which in turn is priceless in strongly regulated industries such as healthcare and finance.
By placing data in object storage, organizations are afforded the control and security they desire on-premises and across hybrid clouds-for those that believe control of the data is a competitive advantage.
Conclusion
With Artificial Intelligence continuing to dictate the data infrastructure demands, the need for scalable, cost-effective, and secure data storage have never been greater. Object storage has proven itself as the bedrock for AI data environments, with the scale, simplicity, and performance that today’s enterprises require.
The journey to exabyte-scale AI infrastructure will be nothing less than daunting; but with MinIO’s object storage solution, companies can future-proof their data infrastructure for the AI demands of tomorrow. Whether in automotive, healthcare, or financial services, object storage can meet the demands of a data-intense, performance-driven environment for organizations on the leading edge of AI.
As all data volumes continues to grow, companies investing in AI infrastructure will be evaluating their storage solutions with a focus on scale and economics. Object storage provides the optimal solution for AI workloads by delivering high-performance capabilities that meet today’s data demands while preparing for the exponential growth expected in the future. MinIO is a key player in this market and gives organizations the ability to build these environments based on their own hardware wishes and hybrid cloud requirements.
More information can be found on the MinIO website, and make sure you watch the MinIO videos from AIDIFD 1 below, or on the Tech Field Day website: