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AI in Adaptive Learning

# AI in Adaptive Learning: Server Configuration

This article details the server configuration required to support an Adaptive Learning system powered by Artificial Intelligence (AI). This setup assumes a moderate-sized implementation serving approximately 500 concurrent users. Scalability considerations are included where appropriate. This guide is aimed at system administrators new to deploying AI-driven educational platforms on our MediaWiki infrastructure.

Overview

Adaptive Learning utilizes AI algorithms to personalize the learning experience for each student. This requires significant computational resources for model training, inference, and data storage. The server infrastructure must be robust, scalable, and capable of handling large datasets. We'll focus on the key components: Application Servers, Database Servers, AI/ML Processing Servers, and Storage. Understanding the interplay between these is crucial for optimal performance. See also System Architecture for a broader overview of our platform.

Application Servers

Application Servers handle user requests, manage sessions, and interact with the database and AI/ML processing servers. They are the primary interface for students and instructors.

Specification Value
Number of Servers 3 (Load balanced)
CPU Intel Xeon Gold 6248R (24 cores/server)
RAM 128GB DDR4 ECC Registered
Operating System Ubuntu Server 22.04 LTS
Web Server Apache 2.4
Application Framework PHP 8.2 with Symfony framework

We utilize a load balancer (e.g., HAProxy or Nginx) to distribute traffic across these servers, ensuring high availability and responsiveness. Consider using a Content Delivery Network (CDN) for static assets to further reduce load. Regular monitoring using Nagios or Zabbix is essential.

Database Servers

The database stores student data, learning materials, and AI model metadata. A robust and scalable database solution is paramount.

Specification Value
Number of Servers 2 (Primary/Replica)
Database System PostgreSQL 15
CPU Intel Xeon Silver 4310 (12 cores/server)
RAM 64GB DDR4 ECC Registered
Storage 2TB NVMe SSD (RAID 1)
Replication Asynchronous Replication

PostgreSQL is chosen for its reliability, data integrity features, and support for complex queries. Regular backups are performed using pg_dump. Database performance is monitored using pgAdmin. Consider using database sharding for extremely large datasets. Proper database indexing is crucial for query performance.

AI/ML Processing Servers

These servers are responsible for running the AI/ML algorithms that power the adaptive learning system. This includes model training, inference, and data preprocessing.

Specification Value
Number of Servers 4 (Dedicated to AI/ML)
CPU AMD EPYC 7763 (64 cores/server)
RAM 256GB DDR4 ECC Registered
GPU 4x NVIDIA A100 (40GB VRAM/GPU)
Operating System Ubuntu Server 22.04 LTS
AI/ML Frameworks TensorFlow, PyTorch, Scikit-learn

These servers are equipped with high-performance GPUs to accelerate AI/ML computations. We use Kubernetes to orchestrate the deployment and scaling of AI/ML models. Model versioning is managed using MLflow. Monitoring GPU utilization is critical, using tools like nvidia-smi. Consider using dedicated message queues like RabbitMQ or Kafka for asynchronous task processing. See also GPU Configuration Guide.

Storage Infrastructure

A scalable and reliable storage infrastructure is required to store large datasets of student data, learning materials, and AI model artifacts.

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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