*Result*: Optimizing AI/ML Model Deployment Across Distributed Systems: Advances in Training Efficiency, Inference Performance, and Fault Tolerance.

*AI and machine learning have grown so fast that computing systems have had to be completely redesigned. Single computers can't handle the massive datasets and complicated model structures that today's AI needs. Distributed computing became necessary—not just helpful—for training and running sophisticated models that go way beyond what one machine can do. Three things matter most: faster training through splitting up work, better inference by managing resources smarter, and keeping everything running reliably even when parts fail. Data parallelism works together with federated learning and compression tricks to scale AI while dealing with communication delays, privacy requirements, and limited resources. Actual working systems—like recommendation engines, language models, tools that predict when machines break, and applications in different industries—show distributed AI really delivers. Companies can build models together now, keep sensitive data private where regulations demand it, and make AI available to more people by using computing power more efficiently. [ABSTRACT FROM AUTHOR]*