The Foundation of Your AI/ML Infrastructure

Table of Contents

  1. Summary
  2. The Foundation Layer for Data Reusability and Transformation
  3. NetApp End-to-End Stack for AI/ML Applications
  4. Key Takeaways

1. Summary

Data creation, storage, and consumption have dramatically changed in the last few years, from both consumer and enterprise perspectives. Most human- and machine-generated data are created, used for a short time, and then stored for an extended period, sometimes forever. From this point-in-time, data quickly loses its value and becomes a liability. The ever-growing capacity need is only the tip of the iceberg. Compliance, privacy, regulations, and an organization’s policies are making everything more challenging, and traditional solutions are no longer sufficient.

IT managers and executives facing these obstacles focus on how to build sustainable and smarter data storage infrastructures. The final goal is not only about storing data safely at reasonable costs for a longer time, but also creating a data foundation layer to support the transformation of every type of data from a liability to an asset. In this regard, big data, Machine Learning (ML), and Artificial Intelligence (AI) force enterprises to rethink several processes to make them faster and more efficient. Enterprises need the right storage infrastructure to unleash this potential.

In order to build the data foundation layer necessary to support big data and ML initiatives, the data storage infrastructure design should offload and standardize several tasks usually performed in the upper layers of the stack. To make this happen, we need a strong combination of:

  • Performance: Metrics, like IO Operations per Second (IOPS) and latency, are now joined by parallelism and throughput. Data is created and consumed by multiple users and devices concurrently for every single application, and in some cases (like IoT), thousands or millions of sensors creating unstoppable structured and unstructured data flows.
  • Capacity: The petabyte-scale is much easier to reach than in the past. It is not uncommon to see these kinds of capacities deployed even in the smallest of organizations, with exabyte scale becoming more common in conversations with very large organizations across all industries.
  • Ease of access: Traditional file systems were not designed to be accessed remotely, across long distances, given unpredictable network latency. Additionally, very few of them can manage large capacities and the number of files in a single domain without some trade-off.
  • Intelligence: Rich metadata is a fundamental component for making data indexable, identifiable, searchable, and eventually, reusable. Metadata enrichment is simply not possible in traditional storage systems. One of the most demanding and time-consuming processes of modern data analytics and ML is the Extraction, Transformation, and Load (ETL) phase of the entire workflow. Automating and offloading this process to the storage system, simplifies these operations, and makes data easier to find and quickly reusable.

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