ZenML Guide 2026: Production MLOps Pipelines Without the Lock-In
ZenML is an open-source MLOps framework that lets you define ML pipelines once in Python and run them on any infrastructure — local, AWS, GCP, or Azure — by swapping a stack configuration rather than rewriting code. In 2026, it’s the most direct answer to the 85% of ML models that never reach production. Why 85% of ML Models Never Reach Production (And How ZenML Fixes That) The production gap in machine learning is one of the most persistent problems in the industry, and the numbers remain damning in 2026. Research consistently shows that 85% of ML models never make it to production, and approximately 45% of ML projects fail specifically due to poor monitoring and retraining pipelines. The root cause is almost never the model itself — it’s the infrastructure around it. Teams build a model in a Jupyter notebook, spend months trying to productionize it using SageMaker, Vertex AI, or a custom Kubeflow cluster, and then discover that any infrastructure change requires rewriting their entire training logic. The research-to-production handoff becomes a six-month project every single time. ...