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Best Practices For Creating An AI Infrastructure Architecture For Modern Data Systems

An AI infrastructure architecture is represented as digital robot with a glowing neural network overlaid on a city skyline at night.

Enterprise AI adoption is on the rise. Gartner predicts AI software spending will reach $62 billion in 2022 alone. AI has many exciting use cases, including in business intelligence (BI) applications and robotic test automation for DevOps CI/CD software development. However, to use artificial intelligence effectively, you need an AI infrastructure architecture that can support your AI application’s data, networking, processing, scaling, and security requirements. In this blog post, we’ll cover six best practices for creating and implementing this architecture.

6 Best Practices for Creating an AI Infrastructure Architecture

Here are six best practices to consider as you build out your AI infrastructure architecture.  

Data Storage

AI requires and produces massive amounts of data, which means you need a data storage system that can scale without limits. Many AI infrastructure architectures use object-based storage rather than traditional file storage to meet this need for scalability. 

Object storage: 

  • Bundles data into objects, along with customizable metadata tags and a unique identifier. 
  • Stores objects in a flat address space that’s infinitely scalable – all you have to do is add additional nodes. 
  • Makes it easier to quickly locate and retrieve specific data by using flat addresses.

Data Processing

An AI application requires a large and constant flow of quality data in order to train and to perform its intended functions. Transporting data from its source to the artificial intelligence application – as well as formatting or transforming that data so it’s usable – can be very challenging at the scale needed for AI. 

The best practice for AI data processing is to use automated tools and pipelines to streamline data ingestion and handling. Using a data processing pipeline, you can automate the discovery, analysis, transportation, and transformation of AI data. Automated data processing allows your AI to ingest more data faster while maintaining data integrity and readability.

Networking Infrastructure

An AI’s neural network is highly dependent on communications between object storage nodes, containers, applications, and other components. Because communication needs to happen almost instantaneously and without interruptions, you need a scalable network with high bandwidth and low latency. 

One way to ensure optimal network performance at all times is with software-defined networking (or SDN). SDN abstracts the management of enterprise networks and decouples it from the underlying hardware, which allows you to employ automation and orchestration. Network orchestration with intelligent routing enables your AI to communicate efficiently without negatively impacting the performance of other systems and services on your network.

Compute/Processing Power

An AI application also requires enough compute power to process and make sense of all the data you feed it. An ideal AI infrastructure architecture uses GPUs, or graphics processing units, in place of traditional CPUs. GPUs use parallel data processing across a large number of computational cores, which means they’re better at performing many similar computations at the same time than CPUs are. 

This processing power makes GPUs are a perfect fit for: 

  • AI 
  • Neural networks 
  • Natural language processing (NLP) 
  • Robotics.


An AI application’s data and compute requirements will only grow larger over time as the algorithms and neural networks learn and get more sophisticated. That’s why scalability is a huge priority for AI hosting and deployment. 

A cloud native architecture provides an infinitely scalable environment for artificial intelligence applications and data. Cloud native infrastructures use containers to create modular and elastic environments for AI applications and their interdependencies. Containers run independently of each other and can be created, deleted, and copied infinitely and automatically to scale on-demand.


Every component of your AI infrastructure architecture needs to be defended from attacks to prevent AI-specific risks such as data poisoning. Data poisoning is when malicious actors feed an AI application bad data on purpose to affect its decision-making capabilities. You also need to ensure that your AI has fast and efficient access to necessary data and systems without leaving any vulnerabilities for hackers to exploit. 

The best practices for AI security include methodologies like:

Some security tools even use AI technology like neural networks to better analyze and detect signs of a breach.

Support Your AI Infrastructure Architecture with DevSecOps

These six best practices will help you create an AI infrastructure architecture that supports your artificial intelligence use cases. Another important (but often overlooked) best practice is to use DevSecOps to build a fully integrated and collaborative team of developers, security analysts, testers, and engineers all working together to achieve the same AI goals. DevSecOps eliminates informational silos and uses automation and cloud native technology to allow large teams to work simultaneously on complex applications and architectures.