Accelerating AI Modernization with Data Infrastructure

Artificial intelligence (AI), Machine Learning (ML) and often Deep Learning (DL) capabilities are now essential components of digital transformation initiatives. The business opportunities that can be achieved with AI are exceptionally promising. Enterprise organizations are increasingly aware that not acting on AI could potentially be a business disaster as competitors gain a wealth of previously unavailable insights and capabilities to grow and delight their customer base. Few if any businesses today believe that “AI is not for us” or that “AI is mostly hype”. Rather, serious AI initiatives are being undertaken worldwide, across industries, and across company sizes.

Businesses are seeking an AI-led transformation and modernization initiative, which involves moving from experimenting to generating business value from their AI investments. The success business investments in AI-related digital transformation are directly tied to the breadth of expertise required to develop, implement, and maintain AI solutions at scale. Many organizations’ lines of business (LOB), IT staff, data scientists, and developers have been working to learn about AI, understand the use cases, define an AI strategy for their business, launch initial AI initiatives, and develop and test the resulting AI applications that deliver new insights and capabilities using machine learning algorithms, especially deep learning.

As organizations scale these initiatives, new questions emerge. They know – indeed, they may have experienced first-hand – that they cannot use standard, general-purpose computing, and existing or legacy storage infrastructure. Also, they realize that AI training (the training of the AI model) and AI inferencing (the use of the trained model to understand or predict an event) require different types of scalable compute with an equally scalable storage infrastructure. While businesses have a better handle on compute, they often underestimate the value of storage in AI. Further, AI applications and especially deep learning systems, which parse exponentially greater amounts of data, are extremely demanding, require powerful parallel processing capabilities based on large numbers of computational cores, and standard storage systems cannot sufficiently enable the execution of these AI tasks. Finally, how such initiatives factor into modernization efforts that include Kubernetes and/or containers, and integration with one or more clouds by way of a hybrid cloud architecture.

IDC research shows that, in terms of storage infrastructure, improper or inadequate attention to detail can quickly derail AI transformation initiatives. To overcome this gap, businesses – that have experimented with existing infrastructure, and are now ready to scale this into production – must overhaul their infrastructure to obtain the required parallel processing performance and do so by investing in more modern storage solutions that scale-out for massive scale and integrated into the cloud, containers, and performance-intensive compute for both global deployment and data access. This is where solutions like IBM Spectrum Scale and IBM Elastic Storage System (ESS) provide the necessary components for an AI information architecture. It is suited for AI workloads, containerized deployment and a hybrid cloud deployment specifically focused on AI workloads.