AI Is Here. Is Your Storage Ready ?

Preparing your storage to handle AI workloads begins with a plan and a common data pipeline. Learn how to get started.

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Artificial intelligence (AI) is driving evolution in every industry. For example, AI can now diagnose and differentiate two of the most common types of lung cancer with 97% accuracy, leading to faster, more appropriate treatment.[i] It is having similar impact from manufacturing to financial services and government. Your AI plans might not yet be solidified, but rest assured that your competitors are working hard to implement theirs. So be ready: You already know you need smart software and modern computing resources to implement AI. Don’t let storage surprises trip you up.

Can your storage handle the demands that AI will place on it? The days of adding more drives to address growing data needs are over because more hard disk drives (HDDs) won’t solve your AI storage needs. When these drives scale, performance-per-gigabyte decreases, risk of failure from moving parts increases, and you are left with a large, inefficient, expensive footprint of dead-end technology. PCIe SSDs are the smarter choice because, with PCIe, AI’s unpredictable mixes of random and sequential reads and writes across variable workloads and fluctuating sizes become manageable.

The future success of your business may very well ride on your ability to deploy AI solutions effectively, but three major storage challenges could stand in your way:

  • Data volume: When it comes to AI, the key to developing proper algorithms lies in amassing data sets. The larger the data set, the smarter the algorithm. To handle that increasing volume of data, storage systems need to scale without limits.
  • Data velocity: Systems must be able to access data instantly and process it in near real time using analytics and machine learning.
  • Data variety: To sharpen their competitive edge and improve processes, businesses are analyzing data across a wide range of formats from a growing array of sources, including the Internet of Things (IoT) and social media.

Overcome Data Challenges with Storage Architecture Tuned for AI

The foundation for resolving these challenges is a common data pipeline that underlies the various AI functions with two tiers: one optimized for space-efficient capacity and scaling, and another optimized for performance and scaling. With the right pipeline, you can begin to resolve your current data issues—and be better equipped to address complicated AI data-storage architecture.

That architecture includes modern, AI-ready storage technologies that provide the low latency and high throughput that AI workloads demand. You will also want to optimize the architecture for highly variable AI workloads. For example, data ingestion is typically a 100% write procedure, followed by data preparation, which can be a 50/50 split between reads and writes, and finally followed by inference or training, which are typically 100% read operations.

With your growing data, you might already have the raw ingredients you need to take advantage of AI. Don’t let those ingredients go to waste. Click here for more strategic advice on how to plan an AI-ready storage infrastructure.

[i]“Using Artificial Intelligence to Classify Lung Cancer Types, Predict Mutations.”

Copyright © 2019 IDG Communications, Inc.