ML System Optimisation

ML System Optimisation #

ML System Optimisation studies how to make machine learning workloads faster, more scalable, more memory-efficient, and suitable for different hardware platforms.

The subject connects machine learning algorithms with the systems that train and deploy them: multi-core CPUs, GPUs, distributed clusters, cloud platforms, edge devices, and embedded systems.

ML system optimisation = model quality + computational efficiency + hardware awareness + scalability

The learning path begins with performance measurement and parallel computing, progresses through distributed machine learning and scale-out platforms, and concludes with model compression and resource-constrained deployment.