Slide 51
Slide 51 text
© Fraunhofer IESE
51
Quality Attributes in ML-based Systems (2/2)
◼ Fulfil the respective quality attributes of the system, respecting the overall “scale” of the system
◼ Performance (latency, throughput), scalability, …
◼ Considering the runtime system, but also the devtime / learning system
◼ Completely different settings for quality attributes in different systems
◼ Playing “Go” against the world champion
◼ a single complex task with massive power needed
◼ Calculation of product recommendations of Amazon
◼ a single, rather simple task; however, executed massively in parallel
◼ Provide an adequate execution environment
◼ Sufficient computing power
◼ Sufficient storage capacity
◼ Provide the right data with adequate frequency and latency
◼ Architect has to know the requirements / implications of the ML algorithm / model