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[ML] Making it easier to understand what Threshold rules exists #69674

Open P1llus opened 4 years ago

P1llus commented 4 years ago

After working more on creating ML jobs for testing, customer demo's and such, there was one thing I found myself constantly doing with threshold rules.

This is a list of issues or scenarios I found, though a bit unsure how we want to resolve it (and if we want to):

  1. I could not set a threshold in the UI during the creation of the first job, if I already know the dataset and its structure, but deleted it due to maybe adding unintended or skewed data into it, I would like to apply the filters and threshold already there.

  2. There was no way for me to know exactly which job had which rule applied to it. For example let's say I create an initial job without threshold, then find out I need to apply certain threshold, so I apply them and then clone the job, and I know have both the old and new job. When doing this multiple times, I actually ended up confusing myself because of bad groupnames or naming conventions of the job, which rule is actually active on each job. I then had to click on each and every job and see which rule it currently had assigned.

  3. The text was a bit confusing around threshold when creating it, the explanation on the UX page says that the conditions are to filter what you want to look for. I thought first that means that anything the condition/threshold hits, that is what is included in the model and results, but it was actually the other way around, which was totally fine but the text and the outcome does not complement each other :(

Possible ways to resolve it, though just general ideas: Make thresholds a saved objects similar to your current filter lists, that way we can reuse thresholds if we want to, but also see which rule they apply to?

Maybe the list of jobs could have some sort of drop-down to display its current thresholds, instead of having to click on each job to see it?

When adding a threshold, it would be awesome if it could include a option to delete the old job and rerun it with the same name and new rules, instead of ending up with maybe 5-10 jobs because you want to tweak the outcome each time to make it fit properly.

elasticmachine commented 4 years ago

Pinging @elastic/ml-ui (:ml)