Analytics, AI and Deep Learning continue to make extensive inroads into data-oriented industries presenting significant opportunities for Enterprises and research organizations. However, the potential for AI to improve business performance and competitiveness demands a different approach to managing the data lifecycle. Here’s five key areas to strongly consider when creating and developing an AI data platform that ensures better answers, faster time to value, and capability for rapid scaling.

Saturate Your AI Platform

Given the heavy investment into GPU based compute systems, the data platform must be capable of keeping Deep Learning systems saturated —across throughput, IOPS, and latency—eliminating the risk of underutilization of this resource.

Saturation level I/O means eliminating application wait times. In storage, this requires different, appropriate responses depending upon the application behavior: GPU-enabled in-memory databases will have lower start-up times when quickly populated from the data warehousing area. GPU-accelerated analytics demand large thread counts, each with low-latency access to small pieces of data. Image-based deep learning for classification, object detection and segmentation benefit from high streaming bandwidth, random access, and, fast memory mapped calls. In a similar vein, recurrent networks for text/speech analysis also benefit from high performance random small file access.

Build Massive Ingest Capability

Ingest for storage systems means write performance and coping with large concurrent streams from distributed sources at huge scale. Systems should deliver balanced I/O, performing writes just as fast as reads, along with advanced parallel data placement and protection

Flexible and Fast Access to Data

As AI-enabled data centers move from initial prototyping and testing towards production and scale, a flexible data platform should provide the means to independently scale in multiple areas: performance, capacity, ingest capability, lash-HDD ratio and responsiveness for data scientists. Such flexibility also implies expansion of a namespace without disruption, eliminating data copies and complexity during growth phases

Scale Simply and Economically

Integration and data movement techniques are key here – a successful AI program can start with a few terabytes of data and ramp to petabytes. While flash should always be the media for live AI training data, it can become uneconomical to hold hundreds of terabytes or petabytes of data on all-flash. Alternate hybrid models can suffer limitations around data management and data movement. Loosely coupled architectures that combine all-flash arrays with separate HDD-based data lakes present complicated environments for managing hot data efficiently. Choose a strategy according to demand; either scaling with flash-only, or combining with deeply integrated HDD pools, ensuring data movement transparently and natively at scale.

Selecting a Partner Who Understands of the Whole Environment

Any AI data platform provider chosen to help accelerate analytics and Deep Learning must have deep domain expertise in dealing with data sets and I/O that well exceed the capabilities of standard solutions, and have the tools readily at hand to create tightly integrated solutions at scale. DDN has long been a partner of choice for organizations pursuing data-intensive projects at any scale. Beyond technology platforms with proven capability, DDN provides significant technical expertise through its global research and development and field technical organizations.

Drawing from the company’s rich history in successfully deploying large scale projects, DDN experts will create a structured program to define and execute a testing protocol that reflects the customer environment and meet and exceed project objectives. DDN has equipped its laboratories with leading GPU compute platforms to provide unique benchmarking and testing capabilities for AI and DL applications.

Contact DDN today and engage our team of experts to unleash the power of your AI projects.