Question 4
Dhoni is on the crease with a bat in hand that has sensors embedded throughout. The sensors talk to the spider cam every second. The spider cam is itself a powerful ARM-based computer which has connectivity to the cloud 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. There is a machine learning model which suggests to the batsman to loosen the grip on his bat or tighten it based on the shots played using the sensor measurements. The way the suggestion happens is using dynamic vibration intensity communicated to the sensors embedded in the bat handle. Your task is to design the data pipeline that enables such feedback to Dhoni ideally before every ball with as much accuracy as possible throughout the match. Which of the following options best satisfies the requirements?
Ingest all data into Pub/Sub, process using Google Cloud Dataflow, invoke the ML model, and then write back output from Cloud to the spider cam to relay to the bat.
Compress the ML model to fit into the spider cam’s available resource, and write pipelines to execute the model in the spider cam itself
Compress the data in the spider cam every 5 seconds, write to Pub/Sub the compressed data, invoke the ML model and then write back output from Cloud to the spider cam to relay to the bat.
Compress the ML model to fit into the spider cam’s available resource, and write pipelines to execute in the spider cam itself, with periodically data being sent to the cloud, retrain the model using Google Cloud ML and then redeploy the model to the spider cam.