There’s no single approach to an IT infrastructure — components and strategies vary depending on the organization’s industry, size and strategic goals.
Today’s scalable AI infrastructure is best represented using a hybrid cloud, a containerized modular AI software architecture, high-performance computing components, strong data security and model management using an AI-focused paradigm such as MLOps.
The following are 10 best practices for scalable AI infrastructure:
A public cloud can provide extensive high-performance computing (HPC) resources on demand using a pay-as-you-go approach. This approach is ideal for ingesting vast data sources and completing training and testing tasks cost-effectively. Public clouds offer elastic compute resources and highly automated scaling features, enabling businesses to adopt an aggressive go-to-market strategy for their AI platforms without the time or cost of building complex infrastructure in-house.
Containers have revolutionized software design, enabling complex workloads to be built from smaller, independent modules that can be invoked and connected as needed. This approach ensures resource efficiency and scalability. Well-established tools like Docker and Kubernetes are available — including as public cloud services — for containerizing and orchestrating modular AI software systems.
AI systems are best served using compute instances that provide advanced HPC components. GPUs, TPUs and NPUs are required to support the rapid, large-scale training and low-latency inference requirements of AI systems. Instances that incorporate GPUs, TPUs and NPUs can be more expensive than traditional CPU-based instances, but each task completion is more time- and cost-efficient.
From autonomous vehicles to humanoid robots, AI is increasingly decentralized, gathering, storing and processing data closer to where it’s created. AI software design must accommodate a distributed computing environment, enabling edge deployments to process data in real time, reducing latency and easing the load on centralized infrastructure. An edge architecture profoundly affects how resources are provisioned and scaled.
The AI lifecycle is an iterative and structured process used to develop and manage AI systems. This complex environment can be error-prone and difficult to manage manually, making automation and orchestration key to achieving consistent, successful outcomes. Approaches such as MLOps can play a central role in AI lifecycle management, ensuring adequate resource provisioning, reliable scalability, proper testing, consistent deployment and ongoing AI performance monitoring.
AI is nothing without high-quality data. AI systems must have effective data ingestion mechanisms, data storage and protection processes and data retention policies. This requires scalable data storage resources, along with a comprehensive data management platform to provide insight into how much data is present, how it’s used, its relative quality and whether it’s protected. Public cloud providers offer data storage, but third-party tools might be needed to implement comprehensive data management.
Security is critical to protect the data used to train and operate AI systems and ensure that only authorized users have system access. Strong access controls, data encryption and other strategies all play a role in security. Security is also a pivotal element of any regulatory compliance strategy, ensuring that sensitive information is safeguarded in accordance with prevailing regulations. While security itself isn’t about infrastructure scalability, scaling without security can introduce vulnerabilities that put data, the AI system and the business at risk.
AI governance includes ethical standards, AI transparency goals, data bias mitigation, safeguards for data use and guardrails for infrastructure use. All these factors relate to fair and responsible AI use, but they also translate into scalability. First, global regulations are increasingly directed toward AI fairness and transparency — businesses might soon need to prove AI operational behaviors and data quality with measures that will stand up to regulatory review. Second, AI governance also includes careful examination and enforcement of infrastructure provisioning and scaling to balance performance and reliability with risk and cost.
Monitoring underpins AI lifecycle workflows. AI developers can see how models drift and outcomes degrade as data changes over time. Monitoring can trigger resource scaling as performance requirements change. It can also trigger AI retraining cycles that need new quality data — both of which demand careful resource provisioning and scaling to achieve. Automation can make AI performance optimization and retraining seamless while maintaining established cost constraints.
Correlating public cloud costs to specific workloads, users, groups or departments can be a challenge. Without specialized tools and cost-optimization practices, cost control and containment can be difficult. A competent cross-disciplinary FinOps team can help identify and assign cloud costs and offer cost-mitigation strategies that make AI infrastructure scalability more cost-effective. FinOps practices can also help identify underused resources and services, providing additional cost management opportunities for the business.
