Well, "protects". My actual experience with Kaseya is that it's an employee monitoring tool that, in a pinch, can also be used by IT to manage machines remotely.
I'm sorry. This does not constitute a randomized, controlled, double-blind study. You cannot know anything until you've passed that gantlet. Nothing worth knowing is actually true until this step has been achieved.
This makes no sense. Neural nets already tear through tabular data like butter. We're literally dropping out half of the learned parameters. This points to the fundamental inefficiency of the approach. Better regularization is the answer, but it's not about round-robin of existing techniques.
Or Apache Airflow. I'd expect this and KFP to have better integrations that cater to data science. You can use Elyra extension to design the workflows right from JupyterLab.
Not from said company, but did do a workflow tool comparison at my work and we also went with Argo.
Argo had better kubernetes + surrounding ecosystem integration out of the box, it was designed to run containers by default which suited us because we had mixed language workloads. Airflow was mostly Python specific, unless you then ran plugins and extensions, the config/pipeline definition was written in Python which I didn’t want to do after witnessing my teammates write the worst Python I’ve seen in my career, and last time I evaluated it, it depended on a bunch of external, Python specific tools (celery etc) that I had previously found painful to run.
> my teammates write the worst Python I’ve seen in my career
Isn't that fairly common which is why there are "ML engineers" that then productionize (clean up and optimize) the original code to be plugged into a production workflow / pipeline system?
yes, it is. it adapts. firings get less strong as we get used to stimulus, and neurons that fire together wire together. so different connections form on more important info.
