Manual Tuning
Manual tuning is a process where developers or system administrators adjust configuration parameters, settings, or code manually to optimize the performance, efficiency, or behavior of software, systems, or algorithms. It involves iterative testing and refinement based on expertise and observation, rather than relying on automated tools or algorithms. This approach is commonly used in areas like database optimization, machine learning hyperparameter tuning, and system performance tuning.
Developers should use manual tuning when dealing with complex, domain-specific systems where automated optimization tools are insufficient or unavailable, such as fine-tuning database queries for specific workloads or adjusting hyperparameters in machine learning models to improve accuracy. It is also valuable in performance-critical applications where precise control over system behavior is required, like optimizing server configurations for high-traffic web applications or tuning real-time processing pipelines.