mailjenil

I really like outliner apps because they incentives to structure notes in an hierarchal fashion. It really helps with problem solving and learning new skills.

I am looking for self-hosted option of outliner apps. I checked and Dynalist and Workflowy dont support self hosted version.

How do you make your code resilient to features in Machine learning model?

Current Model: Model A New Model: Model B

Example 1: If Model A has 10 feature inputs. However, Model B has only 6 feature inputs. No new features added. You have to make production code change remove using other 4 features in Production which requires a code change. How to make system intelligent to take only 6 inputs.

Example 2: If Model B doesnt workout well, you rollback to Model A. How do you ensure that all features are used again in Model A without any code changes and re-adding features.

How do you ensure that code is always compatible no matter what model you use in production.

My workflow for batch/offline training takes a day. I have a use case where I want to aggregate feedback from last hour and perform online learning.

How would you do it in Production ML system serving live traffic?

I have been adding around 20 bookmarks daily and its getting tough to manage all bookmarks. I normally use Firefox bookmark extension.

I often name a bookmark and then forget that name. Its a pain when you want to search for them because you dont remember what you named the bookmark.

For other services like pinboard, you have to tag it manually which is time consuming. I dont want to tag all bookmarks. Its kind of like distraction.

How do you deal with huge number of bookmarks?

I am learning spark and interested in real world applications of common crawl data. If you have huge compute power and common crawl dataset, what would you do?