Managing Machine Learning Models in Production

Building a machine learning model is only the first step; the real challenge lies in keeping it reliable, accurate, and operational over time. This course guides you through the essential strategies for managing models throughout their entire production lifecycle. You will transition from simply training models to managing them as robust software assets, ensuring they deliver consistent value without degrading in performance. What you'll learn: - Understand the core stages of the machine learning lifecycle from training to retirement. - Register and version models to ensure reproducibility and track lineage. - Deploy models using modern containerization and microservices concepts. - Monitor model performance in production to detect data drift and concept drift. - Establish governance and compliance frameworks to manage model risk. - Implement basic CI/CD patterns for automated model retraining and deployment. You will start with foundational definitions and lifecycle terminology before exploring practical strategies for deployment, monitoring, and governance. The material progresses logically from initial model registration to automated retraining workflows. This course is designed for beginner data scientists, software engineers, and technical project managers looking to understand MLOps without needing advanced mathematics or prior production experience. Start reading today to master the art of production model management.