Applying machine learning to cache algorithm I have an application that receives ~10k of requests per day; each request has multiple parameters and goes through a pipeline with multiple steps and they finish in about 1 hour.
Let's say the requests are like bake_cake [flavor=chocolate] [topping1=strawberry] [topping2=cream]

Given a history of requests from previous days, is there a ML framework that can help me predict the "100 most like requests to arrive today" so I can cache their results? Or some other similar strategy that can help me delivering part of those requests faster?

Ideally it would be based on how much I gain by delivering faster, how much I lose by processing a request that might not come, etc.; but for now even simpler algorithms could be of much help since currently there's no optimization.
You need to validate your assumptions based on data driven analysis


Types of requests received on a day
Types of requests received on a holiday
Pattens for different times (Morning / Evening / Night)
Seasonality / Monthly / Weather based patterns


Post Analysis


Do you see correlation from Data &amp; patterns
Do you see correlation from Data &amp; Location
Do you see correlation from Data &amp; Age / Gender / Locality


ML Models


Do you see correlation from Data &amp; patterns
With the variables we can identity and model a forecast algo


ML Model is not the start. It is the outcome of detailed data and domain driven analysis