Dare ObasanjoThe emotional roller coaster of working in tech these days.
Nate Rich
- 5 Followers
- 28 Following
- 30 Posts
Analytics professional and dad joke enthusiast; I lead a business analytics team for Microsoft Advertising, but I tend to post about everything except work; @nathanrich on most other platforms.
@fxshaw I had the exact same thought process a couple years back, and it's annoying to know that something as mundane as replacing a stove will be seen by some as a political statement.
A great reminder that the purpose of a tech stack is to deliver value to your customers and your business. When companies talk more about their tech stack than what it does for customers then they don’t have a business.
This is a hallmark of web3 & crypto
https://hoho.com/posts/your-stack-is-not-the-product/
Russell L. WilsonWill forever love that, immediately upon learning they were eliminated from the playoffs, the Lions went out and beat the Packers in Green Bay *to advance the team that had just knocked them out of the playoffs* solely out of pure spite. Much respect from #Seahawks fans.
Thomas A. Hegna, Ph.D@alexdupler @threddyrex Congrats!
@nicdex as a new user of Mastodon, I’ve definitely been confused by these accounts. Now that I know what they are, I guess I don’t have a problem with them (though I don’t intend to follow them), but it was certainly confusing at first when I thought I was following a person and instead I was just following a bot.
@carnage4life A lot of folks are going to drag you for that "missing communities" comment, but you're absolutely right. "Follow more people" is pretty lousy advice when the topics and hashtags you follow get almost no traction on here.
@carnage4life Any place that has experienced a lot of recent growth is going to see their median tenure go down, so using this as a gauge for "best/worst places to work" effectively punishes companies for growing.
@kareemcarr Generally speaking, this assessment seems about right. I view ML as an applied science where shortcuts get taken due to limitations of time or resourcing (or, dare I say, expertise). In most real-world applications, it's either too difficult or too time-consuming to achieve super high confidence, so we go with the best signals and models available to us.
On my team and with partners, I try to foster a healthy push-pull between the folks who are content with "good enough" and the folks who push for greater rigor before a model goes live. While I don't want to waste time and resources, I also don't want us putting out crappy models, so IMO it's good to have the debate.