MogwaiAllOnYou
Pgvectorscale: Making PostgreSQL a Better AI Database
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The main thing I hated about working from, apart from not having the space to have a proper desk setup, was just feeling constant anxiety about not feeling productive or maybe more so not being seen to be working. And I don't really know why I experienced this, because we don't have that sort of culture at all. However this lead to poor sleep and stress.
I know a lot of people much prefer working from home, and most of if not all of my coworkers do, but I do feel like a lot of the conversations around WFH, especially from software engineers, is WFH is brilliant with no downsides, and the conversation completely negates the people that dislike it for whatever reason
A couple tips from me though:
1) Use Spotify, and the personal generated playlists that expand out (ie Discover Weekly, and Release Radar), avoid the ones that just play what you listen to a lot, it just reinforces a limited set of songs/albums/artists. And use the 'Fans also like' section of artists. Spotify's recommendations tend to really impress me, especially niche stuff
2) Never prejudge music. I used to do this, and since I grew up and stopped, my music taste has expanded in every direction. Own the embarrassing stuff, it's music for your enjoyment
3) Give everything a chance. Listened to something which was too experimental/loud/ambient/whatever? Come back to it later, some genres and even albums are growers, so never discount anything quickly.
4) There's no bad music. There is music you may not like, there's music which may be poorly produced/poor musicianship/etc, but music is about more than the sum of it's parts
__Edit__
Nearly forgot the most important IMO.
5) Songs are (for the most part) designed to sit in their place as a chapter in an album. They typically make more sense in the context of the other songs, so try and listen to full albums rather than songs or playlists. And make sure to turn off shuffle!
Kubernetes is a hot thing, and people want to use, understand it, and become experts in it. Mostly for professional reasons yes, but it will help attract talent, and will likely help with retention.
So I see it as a bit of a trade off. You might have higher infra costs, and a more complex system (and the things that go with that), but you arguably have a happier team aiding retention and hiring which also has a financial value.
The main reason being on premise deployments. Our workload is primarily machine learning models, as such they are well suited to docker containers due to good support and being able to quickly build images, deploy and tear down.
Additionally, whilst the task would be well suited to a Kubernetes deployment which is ideal in the cloud, on prem we're likely running on a single box running multiple VM's. Hence we use containers within the VM.
It isn't the perfect solution and definitely has it's downsides, but it is good enough and better than the alternatives when considering everything possible.
I wanted to do it for the same reasons as you, I enjoy building stuff, and I am of the opinion that data science without engineering is pretty useless as nothing written has the trust of dev teams which is needed for them to plug things into your ML models/pipelines. Some will argue that DS's can do this with dashboards etc, but in my experience they always seem to be made and not used.
In my case, having sat with software eng teams throughout my (short) career, I ended up picking up conversations and challenges they faced as asked questions about technologies and methods they used. I'd go home and look stuff up - enough to understand benefits and disadvantages of things.
Like you, getting to grips with CI/CD, APIs etc etc I had done in past DS projects.
On the money side, I have stayed exactly the same as I was before changing, but obviously ymmv
Additionally, I would argue that you can tell quite a lot about someone provided they've made an effort with their profile. You tend to know music taste, career, where they're from, and interests.