MLOps Pipeline Architecture: From Experiment to Production

Only 22% of companies using machine learning have successfully deployed a model to production. The other 78% are stuck in what the industry calls "the last mile problem" — a Jupyter notebook that works on a laptop but will never serve a single real prediction. The gap between experiment and production is where MLOps lives. This guide walks through building a complete pipeline — from experiment tracking to model serving and monitoring — with practical code you can adapt to your own stack. The MLOps Maturity Model Before building, understand where you are: Level Description Characteristics 0 Manual Notebooks, manual deployment, no versioning 1 ML Pipeline Automated training, experiment tracking 2 CI/CD for ML Automated testing, deployment pipelines 3 Full MLOps Automated retraining, monitoring, feedback loops Most teams are at Level 0 or 1. This guide gets you to Level 2 and points toward Level 3. Architecture Overview ┌───────────────────────────────────────────────────────────┐ │ MLOps