A Sure-Fire Approach to Building Your Company’s AI

Recommendations for Parallelized & Optimized Environments


Artificial Intelligence (AI) is on the minds of many IT and business leaders, thanks to a growing list of compelling use cases. However, when it comes time to implement, many of them find their enthusiasm replaced with frustration.

Why do some AI initiatives fail? Organizations underestimate the scale and complexity of building systems to support AI workloads. In practice, this means finding and processing enough quality data and choosing the right infrastructure to support it.

Recently, Mike Matchett, CEO and principal analyst of Small Word Big Data, sat down to interview Dr. James Coomer, Senior VP of Product Management with DDN. This video of the interview (on TruthInIt.com) discusses AI challenges and the importance of a parallelized and optimized environment. To better understand Dr. Coomer’s recommendations, we’ll first look at how AI handles data.

The AI Data Lifecycle

Step One: Data Ingestion

Building an AI environment requires the collection of unprecedented amounts of data. Otherwise, without enough information to analyze, an AI application can’t form quality inferences. For example, AI for autonomous vehicles may require as much as 100 terabytes of data – per car, and healthcare AI may need tens of thousands of body scans.

Step Two: Data Labeling

After the data is loaded into storage, it must be labeled and categorized. By creating this metadata, AI can understand the context of the data, which enables algorithms to be formed.

Step Three: Model Training

In this final phase, large amounts of data are fed into the system at a very high rate. This step aids the creation of deep learning algorithms. In essence, the AI learns to ‘think’ or perform multitudes of calculations to automate complex decision-making.

Common AI Challenges

Those that have tried to implement an AI strategy have shared their experiences learned in the building of their AI environment. Here are common challenges that appear when an AI optimized infrastructure is not taken into consideration:


In traditional IT environments, large amounts of data are often stored but never processed all at once. As a result, these environments often can’t scale up to handle the required speed and volume-intensive workloads of AI applications.

New Bottlenecks Appear

Even on the most solid networks and platforms, unforeseen problems can appear when processing AI data. That’s because traditional IT architectures can lack the simplicity needed for AI-sized data volumes.

Application Performance Problems

To develop their AI models even further, or to collaborate with other teams, data scientists often need to share AI system resources with other groups and AI projects. When this happens, many enterprise systems can slow, causing angst within the organization and impacting deadlines.

The Answer: AI-Optimized Solutions at Any Scale

Given the extreme amounts of video, image, and audio data processed by AI applications, parallelized GPUs produced by companies like NVIDIA are optimal. To feed these powerful processors, AI-optimized storage is also essential. Ultimately, non-AI optimized environments can’t match the high-speed, high-volume interaction between GPUs and AI data storage.

That’ where DDN comes in. With its A3I storage platform, DDN has partnered with NVIDIA for years to provide powerful AI environments for industries like financial services, autonomous vehicles, and medical research. Our EXAScaler storage infrastructure is available both on-prem and in all three public clouds (AWS, Microsoft Azure, and GCP) fully optimized to deliver data with high-throughput, low latency and massive concurrency using a shared parallel architecture to create a simple, streamlined filesystem, at any scale.

To learn about how DDN can provide a sure-fire approach to building your company’s AI contact one of our experts today.

About DDN

DDN is the world’s largest private data storage company and the leading provider of intelligent technology and infrastructure solutions for Enterprise At Scale, AI and analytics, HPC, government and academia customers. Through its DDN and Tintri divisions the company delivers AI, Data Management software and hardware solutions, and unified analytics frameworks to solve complex business challenges for data-intensive, global organizations. DDN provides its enterprise customers with the most flexible, efficient and reliable data storage solutions for on-premises and multi-cloud environments at any scale. Over the last two decades, DDN has established itself as the data management provider of choice for over 11,000 enterprises, government, and public-sector customers, including many of the world’s leading financial services firms, life science organizations, manufacturing and energy companies, research facilities, and web and cloud service providers.

  • Paul Wallace
  • Date: May 25, 2021

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