Model Agnostic Workflows Trend 2026

Model agnostic workflows allow data teams to build pipelines that work seamlessly with any machine learning framework. By decoupling model logic from training infrastructure, they enable flexible experimentation without rewriting code. Data scientists and MLOps engineers benefit most, gaining faster iteration, easier model comparison, and simplified deployment across TensorFlow, PyTorch, or custom algorithms.