Edge computing is about putting time-sensitive operations closer to where they will have an impact, typically in a location that minimizes latency and maximizes efficiency. This approach contrasts with cloud computing, which collects and analyzes data in a centralized location for greater control and scalability. The choice between edge and cloud computing depends on the specific use case, considering factors such as performance requirements, data processing needs, and system management complexity. Autonomous vehicles, like driverless cars, often require edge computing due to the need for real-time processing of sensor data to ensure safety and responsiveness. Edge computing offers advantages like faster response times, increased reliability, and dedicated processing for specific tasks, but also presents challenges such as managing deployments, monitoring usage, and troubleshooting issues in a distributed environment. Successful implementation of edge computing requires careful consideration of use cases, DevOps principles, deployment automation, versioning, scaling, and monitoring to ensure efficient management of modern applications and their components.