How To Build Intelligent Data Infrastructure for AI workloads?

AI workloads are bringing new requirements to data infrastructure, marking a significant change compared to the “ML era”. The average scale of an AI dataset is multiple times higher than ML data sets used in training, which triggers a question if the approach to data infrastructure needs to be revisited accordingly, and in respect to massive scale and performance requirements of AI workloads.

In this article, we explore the impact of unstructured data on data volumes, emphasize the shift from ML to AI, and underscore the significance of a forward-looking data architecture for businesses aiming to be data-first in the era of AI. Scale out storage infrastructure plays key role in this process. We will put that in the context of Intelligent Data Infrastructure (IDI).

The Story of Unstructured Data

One of the defining characteristics of the AI era is the exponential growth of unstructured data. It is estimated that even up to 95% of the data that exists today is unstructured. That simply means that it is not really considered as “data” in the context of current data infrastructures. These are images, videos, text documents, social media feeds and other types of “data” that aren’t used as a base for data-driven decision making as of today. AI is changing that with its ability to convert unstructured data into structured data. AI models feed themselves with diverse data types that are invaluable for its training, yet it also poses a significant challenge in terms of storage, processing, and retrieval. All the data that was just left behind in cold storage yesterday, is at the core of data infrastructure today.

Unstructured data, such as images and videos, tends to be larger in size compared to structured data. This exponential growth in data volumes places a strain on traditional data infrastructure, necessitating more scalable solutions. It also comes in a myriad of formats and structures. Managing this complexity becomes a critical concern as organizations aim to harness the insights buried within unstructured datasets. Data Infrastructure’s adaptability is indispensable in handling the variety and complexity inherent in unstructured data.

AI models that leverage unstructured data, especially in tasks like image recognition or natural language processing, require significant computational power. The demand for scalable compute resources becomes paramount, and ability to dynamically allocate resources between storage and compute is key for efficiency at scale. Distinct from traditional Machine Learning (ML) datasets, these AI-scale datasets, in the realm of image recognition, natural language processing, and complex simulations reach massive scales, often come with storage requirements in the hundreds of terabytes. Data infrastructure must be tailor-made for such workloads, enabling dynamic resource allocation and efficient management of these vast datasets.

Introducing Intelligent Data Infrastructure (IDI)

Intelligent Data Infrastructure (IDI) is a novel concept that reimagines the way organizations handle and utilize their data. At its core, it involves the decomposition of traditional monolithic data systems into modular components that can be dynamically orchestrated to meet specific requirements. IDI can be built on the public clouds, in private clouds, on-prem, or in hybrid cloud scenarios. This modular, containerized, and fully portable approach enables organizations to build a data infrastructure that is not only scalable but also adaptable to the evolving needs of AI applications and businesses. Key Components of Intelligent Data Infrastructure (IDI):

• Decoupled Storage and Compute: Intelligent Data Infrastructure (IDI) separates storage and compute resources, allowing organizations to scale each independently. This decoupling is particularly beneficial for AI workloads, where computational demands can vary significantly. By allocating resources dynamically, organizations can optimize performance and cost-effectiveness.
• Metadata-Driven Architecture : A metadata-driven architecture is a crucial aspect of Intelligent Data Infrastructure (IDI). Metadata provides essential information about the data, making it easier to discover, understand, and process. In the context of AI, where diverse datasets with varying structures are common, a metadata-driven approach enhances flexibility and facilitates efficient data handling. Storing and accessing large amounts of metadata might require the ability to scale IOPS without limitations to accommodate for unpredictability of the workloads. Today IOPS limitations are a common problem faced by users of public clouds.
• API-Based Connectivity : Intelligent Data Infrastructure (IDI) relies on APIs (Application Programming Interfaces) for seamless connectivity between different components. This API-centric approach enables interoperability and integration with a wide range of tools and platforms, fostering a collaborative ecosystem for AI development.
• Orchestration and Automation : Orchestration and automation play a pivotal role in Intelligent Data Infrastructure (IDI). By automating tasks such as data ingestion, processing, and model deployment, organizations can streamline their AI workflows and reduce the time-to-value for AI projects. Automation on the storage layer is key to cater to these requirements.
• Portability, tiering and containerization: The portability of workloads has never been better, however portability of data itself (or data infrastructures) has yet to catch up. Kubernetes made it easy to orchestrate the movement of workloads, however it is typically storage and data gravity that makes Kubernetes being used mostly for stateless workloads. The shift of stateful workloads into Kubernetes is consistent with the rise of Intelligent Data Infrastructure. Intelligent storage tiering further allows us to build data infrastructures in the most efficient and agnostic way.

Building for the AI Era

Intelligent Data Infrastructure (IDI), unlike traditional systems, is architected to handle the massive scale of AI datasets. The ability to scale out storage, horizonatally and vertically, coupled with dynamic resource allocation, ensures optimal performance for AI workloads. Future-proofing data platforms is crucial in the fast-paced AI era. Intelligent Data Infrastructure (IDI), with its modular and adaptable design, enables organizations to stay ahead by easily integrating new technologies and methodologies as they emerge, ensuring longevity and relevance.

As AI becomes a driving force across industries, every business is poised to become a data and AI business. Intelligent Data Infrastructure (IDI) facilitates this transition by providing the flexibility and scalability needed by businesses to leverage data as a strategic asset. The modular nature of Intelligent Data Infrastructure (IDI) empowers organizations to adapt to evolving AI requirements. Whether it’s integrating new data sources or accommodating changes in processing algorithms, a flexible infrastructure ensures agility in the face of dynamic AI landscapes.

By decoupling storage and compute resources and dynamically allocating them as needed, organizations can optimize their infrastructure costs. This cost efficiency is particularly valuable in AI, where resource requirements can vary widely depending on the nature of the tasks at hand. While cloud services are becoming commoditized, the edge lies in how businesses build and optimize their data infrastructure. A unique approach to data management, storage, and processing can provide a competitive advantage, making businesses more agile, innovative, and responsive to the demands of the AI era.

How can Organizations Adopt Intelligent Data Infrastructure?

In the era of AI, where unstructured data reigns supreme and businesses are transitioning to become data-first, the role of Intelligent Data Infrastructure (IDI) cannot be overstated. It not only addresses the challenges posed by the sheer volumes of unstructured data, but provides a forward-looking foundation for businesses to thrive in the AI landscape. As businesses strive to differentiate themselves, a strategic focus on building a unique and scalable data infrastructure will undoubtedly be the key to gaining a competitive edge in the evolving world of artificial intelligence. The first step of adopting IDI in your organization should be to identify bottlenecks and challenges with current data infrastructure. Some of the questions one should ask are:

1. Is your current data infrastructure horizontally scalable?
2. Do you face IOPS limits?
3. Are you resorting to the use of sub-optimal storage services just to save costs? (e.g. using object storage because it’s “cheap”)
4. Can you scale out storage and compute resources without scaling storage or vice versa?
5. Are you able to easily migrate data and workloads between various clouds and environments?
6. What is the level of automation in your data infrastructure?
7. Do you use intelligent data services (such as deduplication, automatic resource balancing) to decrease data storage requirements in your organization?

Organizations following the traditional approaches to data infrastructures would not be able to easily answer these questions, which by itself would be a warning sign that they are far from adopting IDI. As always, awareness of the problem needs to come first. At simplyblock we help you to adopt Intelligent Data Infrastructure without the burden of re-architecting everything, providing drop-in solutions to boost your data infrastructure with the sight of AI era.

How can Simplyblock help in Building Scale out Storage System for IDI?

Simplyblock’s high-performance scale out storage clusters are built upon EC2 instances with local NVMe disk. Our technology uses NVMe over TCP for minimal access latency, high IOPS/GB, and efficient CPU core utilization, surpassing local NVMe disks and Amazon EBS in cost/performance ratio at scale. Ideal for high-performance Kubernetes environments, simplyblock combines the benefits of local-like latency with the scalability and flexibility necessary for dynamic AWS EKS deployments, ensuring optimal performance for I/O-sensitive workloads like databases. Using erasure coding (a better RAID) instead of replicas helps to minimize storage overhead without sacrificing data safety and fault tolerance.

Additional features such as instant snapshots (full and incremental), copy-on-write clones, thin provisioning, compression, encryption, and many more, simplyblock meets your requirements before you set them. Get started using simplyblock right now or learn more about our feature set.

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