These days, there seems to be an ever growing number of new tools and products trying to serve a small slice of the ML lifecycle, making the landscape complicated/hard to wade through. I've seen a ton of coverage about the financial/regulatory risks associated with the huge AI venture funding wave (e.g. The big risk behind the AI investment boom - https://www.axios.com/2023/10/23/venture-capital-ai-risk-investment), but not many discussions about the impact to the tools/services/infra used around it.I wrote about the tradeoffs between choosing general purpose solutions over a suite of hyper-specialized tools in a blog here (https://clickhouse.com/blog/clickHouse-and-the-machine-learning-data-layer), and thought I'd share for those who might also find it interesting.
Curious if anyone else is thinking about this?