Agentic ML: Moving from Manual Pipelines to Autonomous AI

Your data scientists spend 80% of their time writing boilerplate for feature engineering, debugging CUDA drivers, and stitching together disparate APIs. The actual "science"—the modeling and insight—is a tiny fraction of the workday. This is the "ML Tax," and it is the primary reason most production models never leave the notebook. For the last decade, we have built MLOps to manage this complexity. However, we haven't solved the problem; we have simply given it a name and a set of tools. The re...