Machine Learning Model Deployment: From Notebook to Production Pipeline

Bridging the gap between ML experimentation and production deployment requires robust infrastructure and processes. Modern MLOps platforms are converging on patterns including containerized model serving, A/B testing frameworks, and comprehensive model monitoring. Teams successful at production ML emphasize feature stores, model versioning, and automated retraining pipelines. Critical considerations include handling model drift, managing inference costs, and ensuring explainability for regulatory compliance. The article examines tools like MLflow, Kubeflow, and vendor platforms, sharing practical deployment strategies for models ranging from simple classifiers to large language models.