[jonmoore]

The Modern Data Stack / MLOps product space was succinctly described by one actually-technical CEO as "vending into ignorance"; the author corroborates this with a commendably candid take:

>Imagine it’s 2021, peak MDS, and you meet the CDO of a large bank. “Oh cool,” she says, “you’re the CEO of a tech company. What does your product do?” What do you say?

>“We build a tool that leverages the power of the cloud to apply standard SQL and software engineering best practices to the historically mundane (but critical!) job of data transformation.”

>“We’re the standard for data transformation in the modern data stack.”

>I will tell you that, empirically, option #2 is more effective.

This tallies with what I've seen from a lot of enterprise CxOs and their teams as technology hype moved from big data and block chain and onto data science/machine learning.

There is so much to write about this, but I'll just recommend "Life Cycle of a Silver Bullet" http://freyr.websages.com/Life_Cycle_of_a_Silver_Bullet.pdf, which deserves more attention than it's had on HN.

[lulznews]

Is MLOps more or less of a thing than prompt engineer?

[jochem9]

MLOps is deploying, monitoring and (re)training ML models. Sits in the DevOps and data engineering space.

Prompt engineering is making generative AI do what you want by crafting the right context. I would put it somewhere in the software and data engineering space, given they will most likely integrate applications with it. MLOps comes into play if you have your own trained or tuned model.

[SheddingPattern]

More of a thing, but it's mostly DevOps.