Latency

ML and DL System Performance

AI, ML
ML System Optimization, MLSysOps, Performance, Complexity Analysis, Throughput, Latency, AI, ML

ML and DL System Performance #

Machine learning system optimisation begins with measurement. Before changing an algorithm, adding processors, or moving work to a GPU, we need to understand what is slow, which resource is limiting performance, and how performance changes as the workload grows.

Course coverage:

  1. Metrics: time complexity, running time, space, throughput, latency, and response time
  2. Performance scaling and tuning: measuring bottlenecks and predicting how a workload grows
  3. Training versus deployment: different objectives, workloads, and constraints
  4. Deployment environments: distributed and cloud systems, embedded devices, and mobile systems

Learning Objectives #

By the end of this page, you should be able to: