• AI-Ready Data Infrastructure Reference Architecture White Paper

    Unleash the Power of AI for the Data Awakening

    AI-Ready Data Infrastructure Reference Architecture White Paper


  • Trend
  • Challenges
  • Definition
  • Reference Architecture
  • Advice for CIOs

Challenges for Data Infrastructure in the AI Era

icon pc 1 001
Data Asset Management

Enterprises need diverse capabilities in order to deal with data of varying quality, drawn from numerous sources, support full data mobility between different departments.

icon pc 2 001

The computing power utilization of a large-scale training cluster is less than 50%. This results in high computing construction costs and high power consumption.

icon pc 3 001

Any user must be able to access the latest updated data copy at any time, on any node.

icon pc 3 001

AI-based applications lead to new vulnerabilities, endangering resilience. There is potential here for a loss of tens of millions of dollars after a large AI model is attacked.

Definition and Characteristics of AI-Ready Data Infrastructure

AI-ready data infrastructure refers to data storage software and hardware systems designed for AI applications and services. Building AI-ready data infrastructure therefore requires comprehensive preparation, across the board.

architecture en 1

Reference Architecture

Central AI clusters can now have tens of thousands — if not hundreds of thousands — of cards. With edge AI models beginning to permeate many industries at a faster pace, Huawei’s AI-ready data infrastructure reference architecture and solution help enterprises across different industries build resilient, reliable, and open AI data infrastructure, empowering them to elevate their intelligence and innovation to the next level.

architecture en 3

Advice for CIOs

  • Build Unified Data Lakes
  • Employ an AI-ready Data Infrastructure
  • Embed All-round Data Protection Capabilities
  • Deploy One-stop Training HCI Appliances
  • Build specialized Technical Teams
  • Build Unified Data Lakes

    AI is only as good as the data that fuels it. Enterprises must build unified data lakes to enable data asset visibility, manageability, and availability, turn data into a key element of production, and implement large AI model services faster.

    Build Unified Data Lakes
  • Employ AI-Ready Data Infrastructure

    Ever-evolving large AI models have increasingly higher computing power needs. Enterprises should refer to industry best practices to shift from simply stacking computing power to fully unlocking its potential. This requires enterprises to build a future-proof intelligent computing foundation powered by an AI-ready data infrastructure solution.

    Employ an AI-ready Data Infrastructure
  • Embed All-Round Data Protection Capabilities

    Data assets have become increasingly critical to large AI models. Tampering, theft, and ransomware attacks to core data such as model files and training data can cause massive resource waste and economic losses. Enterprises should immediately build all-round data protection capabilities.

    Embed All-round Data Protection Capabilities
  • Accelerate AI Adoption via HCI Appliances

    Large AI model training for data center applications requires huge investments. Edge applications are a key area where generative AI can create profits. Enterprises should use one-stop training/inference HCI appliances to quickly develop and launch their AI products to monetize their large AI models.

    Deploy One-stop Training HCI Appliances
  • Build Specialized Technical Teams

    Enterprises should proactively evaluate their AI readiness, build technical teams specialized in large AI models (especially storage for large AI models), and improve their professional capabilities.

    Radar- and Video-Based Trajectory Generation