Most AI teams are over-staffed on ML engineers and under-staffed on ML ops.
For a mature AI product: 1 ML researcher per 3 ML engineers per 2 MLOps engineers per 1 data engineer. Most teams invert this — too many researchers, not enough ops.
Cost implications: ML researcher ($200-350K), ML engineer ($180-280K), MLOps ($160-240K), data engineer ($150-220K). The ops roles are cheaper AND more impactful for production AI.
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11 minPublished Work
This article expands on ideas from my published work in CIO.com, Built In, Mind the Product, and HackerNoon. View published articles →
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