Question 13
You are given a Spark Streaming pipeline that invokes a pre-trained DL model for every image it receives as input and produces the classification result in quick time. The model with the best recall rate from the PyTorch library runs in under 3 seconds on an average, when executing on a GPU-powered Spark cluster. Your management has now instructed you to reduce the cost of AI projects significantly, and has given guidance that latency of execution is not a concern since the consumer for the model output is batch oriented, and also that they would be ok with a lesser recall rate provided the drop isn’t significant. What is the best option to explore first to meet the expectations?
Build a custom model that compresses the highest recall rate model justenough to be able to execute within a single Spark worker and thus reduce cost of the cluster.
Use a different DL model from PyTorch that has better efficiency at lesserrecall levels so that the cluster size can be minimized.
Remove complexity associated with Spark Streaming, remove GPUs to savesignificant costs, and convert the model execution pipeline into a single threaded Python application using traditional ML running on a CPU-only machine.
Change Spark machine to use CPUs and train a fresh pipeline to achieveobjectives.