Organizations today agree that artificial intelligence (AI) is the fast track to innovation and productivity. Most organizations are already on their way to testing, adopting, implementing, and realizing the full potential of AI and, as a result, corporate investment in AI solutions is expected to increase significantly over the next several years.

Every successful AI project goes through a multi-step process that starts with having the right data and progresses to using AI broadly.

Adopting AI is not without its challenges. Open source and commercial developer tools and frameworks make it straightforward to deliver your first artificial intelligence (AI) project or proofof-concept. However, organizations face challenges when supporting AI development teams or deploying and scaling production AI workloads:

  • Data volume and quality. Artificial Intelligence requires high-quality, diverse, and labeled data inputs. Identifying the right data sets across multiple data sources with dynamic data characteristics can be daunting.
  • Advanced data management. Organizing and tracking data sets in AI projects is a challenge for developers who need to repeatedly test, re-use and expand data sets to improve AI model accuracy.
  • Skills gap. The increasing demand for AI services means a corresponding increase in the need for skilled professionals. Since AI is still a relatively new field, it’s difficult to find trained personnel and best practices for data science productivity.

There is no AI without IA (information architecture)

The AI pipeline — how you ingest, organize and analyze data and, ultimately, train models to create AI-driven insights from that data — is essential to efficient data science. Efficiency of your AI pipeline is directly tied to addressing the challenges above with the right IT infrastructure.

  • Unified data access
    Data silos are a major obstacle to the productive use of data, particularly when it comes to AI. Collecting data can be the most time-consuming phase of an AI project. The skills investment in data set organization and classification should be leveraged across all AI projects. It requires a data and storage architecture that minimizes redundancy, improves efficiency and enables common, shared data for multiple projects and supporting the range of data analytics tools.
  • Data throughput performance
    AI model accuracy is a function of good data input and sufficient compute resources to analyze it. Graphics Processing Units (GPUs) are often used for AI because they analyze large data sets quickly. The IT infrastructure must be paired with storage performance to match the compute resources’ ability to consume data. Similarly, streaming data may be used for real-time insights requiring attributes that properly distribute data workloads.
  • Agility with container support
    AI projects are typically managed in containers because they are lightweight, quickly deployed, and can combine multiple programs and scripts. To quickly scale from initial experiments to production-grade AI, persistent storage that works with Kubernetes and Red Hat OpenShift is required. Containers not only simplify development, but also add agility to the IT infrastructure to accommodate growth in the demand for enterprise AI services.
information architecture
information architecture

Building a strong foundation

Growing an AI practice seems complicated, but it doesn’t have to be. AI projects are easier and more likely to succeed if they’re built on a solid foundation. IBM Storage for AI provides that foundation, with a collection of offerings that put you on the fast track to AI productivity by addressing the top business challenges associated with deploying AI workloads.

IBM Spectrum® Scale
IBM Spectrum Scale is a high-performance file system solution that automatically grows with and unifies your storage infrastructure. It is software-defined to balance performance and costs by moving file data to the optimal storage tier quickly and efficiently. IBM Spectrum Scale enables you to securely collect and organize data, providing data-anywhere access with a unified data foundation that simplifies AI adoption.

IBM Cloud™ Object Storage
IBM Cloud Object Storage delivers performance and scalability for cloud native applications and AI frameworks. It is a secure, software-defined storage platform that easily scales capacity and throughput from terabytes to exabytes. IBM Cloud Object Storage is the ideal solution for teams using the latest cloud development environments that also need data security or high-performance local data.

Eight Storage Requirements for Artificial Intelligence and Deep Learning

The journey to AI starts with a single successful proof-of-concept, and can quickly grow across the organization. Navigating that journey successfully starts with creating a robust, agile IT foundation optimized for the unique data requirements that drive productivity and adoption. The right storage platform must deliver performance, scalability, and flexibility, which AI projects demand. The decisions you make as you build that foundation have far-reaching implications that will impact you at every step along the way and, ultimately, determine your success.

That’s why having the right partner from the outset is critical. IBM Storage for AI provides end-to-end optimization of the data pipeline to improve data governance and accelerate time to insights. By combining industry leading offerings, innovation and proven leadership, IBM enables you to build the infrastructure you need to manage your data, handle AI workloads, leverage the power of AI, and ultimately drive deeper insights that create better business outcomes.

To read full download the whitepaper:
Storage for AI: The fast track from ingest to insights

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