cschmid

I just watched the product video (https://www.youtube.com/watch?v=sj1t3msy8dc), and it's clear that surveillance is not a stealthy component, but the only function this device actually has: Despite a $1000 sales tag, they didn't even bother putting a vacuum in their robot.

Amazon's main target audience is suburban helicopter moms who want to snoop on their kids, and I doubt that privacy is a major concern to them.

For a binary classifier, recall is defined as the true positive rate: Of all the samples that are actually positive, how often did our model output "positive".

To get this to 100%, the model would simply say "positive" all the time. This means that the precision (the fraction of samples where the classifier said "positive" that were actually positive) goes to 50% (for a balanced dataset).

Streamlit is used by data science/ML people to turn models into (usually internal) MVPs.

Let's say you implemented your model in python, and want to show it off:

With streamlit, you can simply take your python script, turn the variables you want to change and the plot/dataframes you want to output into streamlit objects. That takes about 5-10 minutes, and then you can already serve your application. It's almost like it's no extra work.

If you want some inspiration, have a look at https://replit.com/site/careers. I believe they're running a docker container, which gives you the full bash experience!