A lot of “AutoML” tools promise automation, but most of them just wrap hacky templates around model training. You still end up with fragile pipelines, unclear evaluation, and a model that works in a notebook but collapses the moment it meets real production data. Plexe is built for the opposite outcome: production-grade reliability, not research-grade demos. With this launch, every pipeline Plexe generates isn’t just trained, it’s validated, stress-tested, and monitored end-to-end. Before a model is ever deployed, Plexe automatically runs: 50+ diagnostics for leakage, drift, bias, and data quality issues Reproducible experiment traces and no more “mystery runs” Monitored deployments by default, not as an afterthought This means you’re not “AutoML-ing” your way into technical debt. You’re shipping something that holds up under real usage, changing data, and real business expectations. If your goal is reliable ML in production, not another Jupyter victory lap, Plexe gives you a deterministic path from messy data to verified model. Curious to see what the community will build when the output is actually production ready and not just automated. Happy to answer questions in the thread. Let’s build production grade models faster!