I come from a strong math/stats background and really enjoy the modeling, analysis, and problem-framing side of data science (e.g. feature engineering, experimentation, interpreting results). What I’m less interested in is the MLOps side — things like deployment, CI/CD pipelines, Docker, monitoring, infra, etc. With how fast AI tools are improving (e.g. code generation, AutoML, deployment assistants), I’m wondering: Can AI realistically automate a large part of MLOps workflows in the near future? Are we reaching a point where a data scientist can mostly focus on modeling + insights, while AI handles the engineering-heavy parts? Or is MLOps still fundamentally something you need solid understanding of, regardless of AI? For those working in industry: How much of your MLOps work is already being assisted or replaced by AI tools? Do you see this trend continuing to the point where math/stats skillsets become more valued by employers? submitted by /u/Excellent_Copy4646
Originally posted by u/Excellent_Copy4646 on r/ArtificialInteligence
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