MLOps
topic on 2 shows · 4 statements across 4 episodes
4 statements about MLOps, every show
Shankar: AI evals differ from MLOps due to data scarcity
“The other thing is I think that AI engineering Evaluation or evals here is actually different from MLOps or ML evaluation for traditional ML models. We were in a much more, you know, data rich setting in MLOps. So we were taught to come up with loss metrics or…”
Enterprise MLOps requires batch retraining and drift management, not just APIs
“A misconception is that moving a model in projection is about turning a model into an API, which is of course, one step, but also in many instances, it's about robustifying models that can be applied in a batch fashion, robustifying the fact of automating the …”
Turck: The MLOps startup category is likely to consolidate
“I think MLOps in general, that's, you know, just in our chart, there's probably, I don't know, 25, 30 companies in MLOps. That's probably going to Consolidate.”
Wu: Point solutions proliferate in early-stage MLOps and ModelOps commercialization
“What, you know, within, within model ops and ML ops tooling is still in pretty early days in terms of commercialization, but from our perspective, we're seeing a lot more point solutions pop up around different parts of the machine learning and you know, broad…”