Building an AI-Ready Infrastructure: Which Technologies to consider ?
Why an AI-Ready Infrastructure Is Essential
Deploying artificial intelligence (AI) at scale requires a robust and purpose-built technological infrastructure. The very nature of AI models—involving massive computations, real-time data processing, and continuous algorithm adaptation—demands far greater processing, storage, and data transmission capacities than traditional IT systems.
Gartner Study
According to a Gartner study, by 2025, 75% of businesses that have adopted an AI-Ready infrastructure will see a 35% improvement in operational efficiency. Moreover, the volume of data generated by AI applications is expected to grow by 40% per year, making it crucial to adopt systems capable of handling this increasing complexity.
What Is an AI-Ready Infrastructure?
It must be capable of:
Managing a wide variety of structured and unstructured data.
Executing complex algorithms in real time.
Ensuring both horizontal and vertical scalability.
Providing resilience to failures and enhanced security.
AI is not only a key technology but also a strategic necessity for companies aiming to maintain long-term competitiveness.
1. Key Components of an AI-Ready Infrastructure
To meet the demands of AI applications, infrastructure must be based on several essential technology pillars:
1.1 High-Performance Computing
Machine learning and deep learning models require high computing power to process real-time data and train models effectively.
GPUs (Graphics Processing Units) are currently the most powerful solution for AI workloads due to their ability to parallelize computation.
TPUs (Tensor Processing Units) are also used for deep learning operations.
High-performance servers equipped with multi-core processors and hardware accelerators (e.g., FPGA) ensure fast execution of complex models.
Examples:
Voice recognition and image analysis models are typically executed on high-performance GPUs for real-time processing.
Tesla uses NVIDIA GPU clusters to train its autonomous driving models.
Running AI models requires significant computing power, capable of handling billions of calculations per second, such as technologies:
Dell PowerEdge servers with NVIDIA GPUs optimized for AI workloads.
IBM Cloud AI enables parallel processing of multiple complex models.
VMware AI Foundation optimizes AI workloads in hybrid environments.
Example :
- AI models for speech recognition and image analysis typically run on high-performance GPUs to accelerate real-time processing.
- Tesla uses NVIDIA GPU clusters to train its autonomous driving models.
1.2 Fast and Flexible Storage
AI models use massive amounts of data that must be accessed in real time.
NVMe storage systems offer significantly faster read/write speeds than traditional systems.
Object storage solutions are ideal for unstructured data (images, videos, documents).
Distributed file systems enable efficient workload management across multiple servers.
Examples:
E-commerce platforms use NVMe storage to accelerate customer request processing and enhance UX.
PayPal uses IBM Spectrum Scale for real-time data processing during transactions.
Data Accessibility
Your AI data must be easily accessible to enable rapid analysis using technologies such as:
- Dell EMC PowerStore, Powerscale et ObjectScale : high-performance storage for AI.
- IBM Spectrum Scale et Spectrum Scale : scalable storage optimized for real-time data analytics.
- VMware Cloud Foundation : centralized resource management in a multi-cloud environment.
Example :
- E-commerce platforms use NVMe storage systems to accelerate customer request processing and improve the user experience.
- PayPal uses IBM Spectrum Scale storage solutions for real-time data processing during transactions.
1.3 Hybrid Cloud Infrastructure
An AI-Ready infrastructure must harness the benefits of both public and private clouds.
Container platforms like Kubernetes enable flexible AI model deployment in hybrid environments.
Multi-cloud management solutions allow seamless workload movement between environments depending on performance and security needs.
Hybrid environments reduce latency by bringing compute power closer to users.
An hybrid infrastructureÂ
It allows you to combine the flexibility of the public cloud with the security of the private cloud, as is the case with the offers:
- Focus Cloud Solutions : hybrid deployments powered by VMware.
- VMware Cloud on AWS : rapid AI model deployment on public cloud.
- Red Hat OpenShift : Kubernetes platform for hybrid environment orchestration.
Exemple :
Financial service firms use hybrid environments to manage regulatory-sensitive AI models while leveraging public cloud flexibility during usage peaks.
Pinterest uses a hybrid VMware-based infrastructure to manage data flow and train its AI models.
1.4 Intelligent and Scalable Networks
Fast and secure data transfer is critical in an AI environment.
Software Defined Networking (SDN) solutions provide smart and automated traffic management.
AI-optimized network architectures enable high bandwidth with low latency and dynamic packet routing.
5G and edge computing technologies reduce latency and accelerate on-site data processing.
A high-performance network
Network performance is essential to ensure the speed of exchanges between servers, storage and cloud platforms.
- DELL POWERSWITCH : high-speed network infrastructure tightly integrated with Dell AI servers.
- Cisco AI-Networking: automated networking for AI workloads.
- Nokia AirFrame : optimized for edge AI data processing.
Example :
- Video streaming platforms use SDN networks to optimize content delivery based on user behavior analysis.
- Spotify uses Cisco network infrastructure to manage AI-driven audio content delivery.
1.5 AI-Enhanced Cybersecurity
AI models are vulnerable to attacks such as data poisoning. An AI-Ready infrastructure must include automated and adaptive security mechanisms.
AI-powered intrusion detection systems (IDS) can identify anomalies in real time.
Zero Trust security frameworks verify every access to data and applications.
Incident response automation ensures rapid mitigation in the event of an attack.
The security of AI models
AI systems are vulnerable to attacks and data manipulation.
Fortinet AI Security: real-time anomaly detection using ML algorithms.
Palo Alto Cortex XSOAR: automation of security incident response.
Example :
Cloud service providers use AI-based IDS to analyze access logs and detect suspicious behavior.
Sony secures its AI content production infrastructure with Fortinet solutions.
2. Concrete Solutions for an AI-Ready Infrastructure
An AI-ready infrastructure combines the following technologies:
Processors: GPUs, TPUs, FPGAs for AI model processing.
Storage: NVMe systems, object storage, and distributed file systems for fast data access.
Hybrid Cloud: multi-cloud platforms and Kubernetes orchestration.
Networks: high-bandwidth Infiniband or Ethernet with SDN controllers and 5G.
Cybersecurity: IDS, Zero Trust, and automated security response systems.
3. How Focus Corporation Supports This Transition?
Infrastructure audit: evaluate AI-specific business needs.
Deployment of AI-ready architecture: choose the right technologies, install and configure systems.
Continuous optimization: performance monitoring and configuration tuning.
Team training: upskilling internal teams for fast and effective AI adoption.
An AI-Ready infrastructure is essential to fully leverage the power of artificial intelligence. Focus Corporation helps its clients define a strategic technology roadmap, deploy tailored solutions, and support internal team skill development to ensure successful adoption.
