AI Infrastructure

What is AI Infrastructure?

AI storage and data infrastructure are evolving rapidly to support the complex demands of training and deploying machine learning models. This technology plays a pivotal role in ensuring optimal performance, scalability, and reliability in AI applications.

AI Storage 

AI storage solutions are specifically designed to handle the unique challenges posed by AI workloads—specifically, efficiently managing the massive datasets used in training models. AI storage solutions prioritize high-throughput, low-latency, and scalable performance. Technologies such as solid-state drives (SSDs), distributed storage architectures, and parallel file systems are commonly employed.

One of the primary challenges in AI storage is maintaining high performance when processing large datasets multiple times, which requires high-speed access to the data. AI storage infrastructure addresses this challenge by providing solutions that can handle the parallel processing needs of deep learning frameworks at the speed which AI applications demand.

AI storage solutions must be highly reliable and deliver features for data replication, snapshots, and backups to ensure the integrity and availability of the training data. Given the sensitivity of the data often used in AI applications, organizations must develop strong data governance guidelines and policies to protect PII and IP data from leakage into commercial tools that can expose this data to other users and organizations.

This blog details five key areas for AI data governance to consider across security, privacy, lineage, ownership and governance of unstructured data for AI – or SPLOG.


Data Infrastructure for AI

Data infrastructure for AI consists of an ecosystem of technologies and processes for managing and manipulating data for AI applications. This includes not only storage but also tools and frameworks for data preprocessing, cleaning, and transformation, along with unstructured data management solutions.

  • Distributed computing frameworks, such as Apache Hadoop and Apache Spark, are integral to AI data infrastructure, delivering parallel processing of data across multiple nodes or servers.
  • Graphics Processing Units (GPUs) also play a crucial role by accelerating the training and inference processes of machine learning models. GPUs, designed for parallel processing, handle the complex mathematical operations involved in training deep neural networks. They work with AI storage to ensure high-speed access to data, reducing latency and improving overall performance. GPUs also play a crucial role in the inference phase, bringing real-time predictions. AI storage solutions must be capable of supporting the high-throughput requirements of GPUs, to prevent data bottlenecks. Specialized AI accelerators from NVIDIA and AMD further enhance parallel processing capabilities with speed, efficiency, and scalability.
  • Unstructured data management solutions play a pivotal role in AI infrastructure by delivering a unified, independent console for holistic data visibility across on premises, edge and cloud storage. An unstructured data management system such as Komprise can deliver a Global File Index for users to conduct ad hoc searches of file and object data to find the precise data sets they need for AI. Komprise also delivers automated workflow capabilities with Smart Data Workflows, so that users can search, tag, and move data to data links and other platforms for use by AI applications—or similarly, to exclude sensitive data from ingestion into AI tools by automatically finding and moving it into immutable, object storage in the cloud.

Learn more about AI and Big Data capabilities in Komprise.

Read the white paper on tactics for managing and protecting data for GenAI.


The Role of the Cloud in AI Infrastructure

Cloud storage services from industry leaders like Amazon Web Services (AWS) and Microsoft Azure also play a significant role in AI infrastructure. These cloud platforms offer scalable and flexible storage solutions that can be tailored to the specific needs of AI applications, such as AWS S3 and Azure’s Blob Storage.

AWS and Azure are also developing an expanding array of AI and machine learning tools and services, creating easily deployable AI-as-a-service offerings for companies.

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As AI evolves, enterprise organizations will look to integrate the right combination of storage solutions, data infrastructure, unstructured data management systems, GPUs, and cloud services to efficiently manage and protect data used for AI initiatives.

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