Is this a scenario that would work well for a ML/Pattern Recognition Model or would it be easier/faster to just filter from a large DB.
I am looking to create a system that will allow users to identify the appropriate product by specifying certain constraints and preferred features.
There are millions of possible product configurations. Lets pretend it's boxes.
- Size (From 1mm up to 1m) in 1mm increments
- Color: choice of 10 colors
- Material: choice of 3, wood,metal, plastic
- Wood is only available in centimeter units
- Red is only available in 500 mm and greater
- Wood is the preferred material
- Blue is the preferred color
So, we have 30,000 (1000*10*3) possible options. Of those, many are not viable such as 533 mm-Red-Wood
but these configurations similar to the request are possible.
- 533 mm-Red-Plastic
- 530 mm-Red-Wood
- 540 mm-Red-Wood
Notes: Our current Rules and code based tool can take anywhere from 0.5 to 2 mins to identify the preferred configuration. We can generate a list of all possible configs and whether they are valid or not. We estimate 30,000,000 possible configs It takes around 0.5 seconds to validate a config so with enough computing power we expect we could do 30M in a few days.