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Autonomous networks Autonomous networks refer to self-managing, self-healing, and self-optimizing network systems that operate with minimal human intervention. These networks leverage advanced technologies like artificial intelligence (AI), machine learning (ML), and automation to autonomously handle tasks such as traffic management, fault detection, performance optimization, and security. The goal is to create more efficient, adaptive, and scalable networks that can respond to changing conditions in real-time while reducing the need for manual configuration and intervention. Autonomous Networks are self-managing systems that use AI, machine learning, and automation to optimize network operations. Key points: 1. Self-Management: Autonomous networks can configure, monitor, and maintain themselves with minimal human intervention. 2. Automation: Network tasks like traffic routing, optimization, and fault detection are automated, improving efficiency. 3. AI and Machine Learning: AI/ML algorithms enable networks to learn from data, adapt to changing conditions, and make decisions independently. 4. Self-Healing: These networks can automatically detect and fix issues (e.g., network failures or congestion) without manual input. 5. Scalability: They can easily scale up to accommodate growth without requiring significant manual reconfiguration. 6. Improved Performance: Autonomous networks can optimize performance in real-time, balancing loads, managing traffic, and ensuring quality of service. 7. Reduced Human Error: By automating processes, these networks minimize the risk of human errors in configuration and maintenance. 8. Enhanced Security: AI-driven networks can detect and respond to security threats faster and more effectively. "Share and repost to help spread knowledge far and wide. Together, we can make valuable information accessible to everyone!" Follow us on TelcoLearn Sanjay Kumar ↗️ for more updates #AutonomousNetworks #AI #artificialintelligence #ML #machinelearning

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