MLOps and Production Machine Learning

The data science field has matured from experimental notebooks to production-ready machine learning systems. MLOps practices are becoming essential for organizations looking to deploy and maintain ML models at scale, ensuring reproducibility, monitoring, and continuous improvement of AI systems.

Modern data science workflows incorporate automated feature engineering, model versioning, and real-time monitoring. The emergence of feature stores, model registries, and automated retraining pipelines is making machine learning more accessible to organizations of all sizes.