Question 10
Fielder on the midwicket boundary is wearing a smart watch. Unlike other smart watches, this one is unique in that it helps him field. Whenever the ball is headed his direction, the watch vibrates to alert him. When there is a chance of a catch by this fielder, the watch continually whispers every second to go forward, back, left or right thus improving his chances of settling under the ball and taking the catch. The watch is connected to the spider cam. The spider cam is itself a powerful ARM-based computer which has connectivity to the Cloud, an all-seeing AI-powered superbeing, through the wire on which it hangs. Using this connectivity, it can send as much or as little data as required and also receive instructions from the cloud. Your task is to design the data pipeline that enables such feedback to the fielder for every ball that comes his way with as much accuracy as possible throughout the match. You are given two ML models: a vision model that given a frame from the spider cam computes an alert if the ball is headed to him, and an adjustment model that helps adjust catch positioning of the fielder which takes as input a continuous video feed. Which of the following options best satisfies the requirements?
Ingest all spider cam data including video feeds into Pub/Sub, process usingGoogle Cloud Dataflow where you invoke the vision model and the adjustment model, relaying this output to the spider cam continuously.
Compress the vision model and adjustment model to fit into the spider cam’savailable resource, and write pipelines to execute the models in the spider cam itself
Compress the vision model and adjustment model to fit into the spider cam’savailable resource, and write pipelines to execute in the spider cam itself, with periodically data being sent to the cloud for retraining the vision model using Google Cloud ML and then redeploy the model to the spider cam.
Compress the data in the spider cam every 5 seconds, write to Pub/Sub thecompressed data, invoke the vision model and, if needed, the adjustment model, and then write back output from Cloud to the spider cam to relay to the fielder.