Foundations of MLOps: Deploy and Monitor Machine Learning Models

Machine learning models only deliver value when they are successfully running in production. However, bridging the gap between a local notebook and a scalable, monitored deployment can be challenging. This course provides a comprehensive introduction to Machine Learning Operations (MLOps). You will start by understanding foundational terminology and lifecycle concepts before progressing to hands-on, text-based exercises. By the end of the program, you will know how to package models, set up continuous integration, and implement monitoring strategies to keep your models accurate over time. What you will learn: Understand foundational MLOps terminology and the machine learning lifecycle. Package and containerize machine learning models for consistent deployment. Configure basic CI/CD pipelines tailored for machine learning workflows. Apply modern model registry patterns to track versions and metadata. Monitor model performance in production to detect data drift and degradation. Design automated retraining loops to keep your models current and accurate. The curriculum flows logically from basic definitions and environment setup to advanced topics like observability and automated retraining. Through clear written explanations and practical code snippets, you will build a robust understanding of production-grade ML systems. This course is designed for beginners, data scientists, and aspiring ML engineers looking to learn deployment skills from scratch, with no prior MLOps experience required. Start building your MLOps foundation today and bring your machine learning models to life.