An online grocery store uses a custom machine learning model deployed on Google Vertex AI to recommend recipes. The model is currently running on a single machine with 8 vCPUs. Anticipating a fourfold increase in traffic during the holiday season, the store needs to scale the model efficiently.
Several options are presented to address the increased demand:
The suggested solution is Option C. This approach leverages autoscaling for efficient resource management and avoids the potential over-provisioning of resources in options B and D.
You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex AI endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.
A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic. You need to ensure that the model can scale efficiently to the increased demand. What should you do?
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