MLOps Foundations: Automated Pipelines with Kubernetes and Cloud Tools

Scaling machine learning from a notebook to a production environment requires more than just code; it requires a robust operational framework. This course provides a clear path for those looking to bridge the gap between data science and reliable software engineering. You will learn how to transform static models into scalable, automated services that can handle real-world data demands. By the end of this course, you will be able to design and maintain end-to-end MLOps workflows using industry-standard tools. You will move from understanding basic versioning to implementing complex container orchestration and continuous integration strategies. What you'll learn: - Understand the core principles of MLOps and the lifecycle of production-grade machine learning. - Manage data and code versioning using DVC and Git to ensure project reproducibility. - Automate model training and deployment workflows with CI/CD tools like Jenkins and GitHub Actions. - Containerize machine learning applications with Docker and orchestrate them using Kubernetes. - Track experiments and manage model versions with MLFlow and centralized registries. - Monitor model performance and detect data drift using Prometheus and Grafana. - Apply modern MLOps patterns including basic LLM observability and vector data management. The course begins with foundational definitions and key terminology before guiding you through the practical application of automation, containerization, and monitoring. You will work through written explanations and code-based exercises that simulate real-world production scenarios. This course is designed for beginners in data science, software engineering, or DevOps who want to learn the operational side of machine learning. No prior experience with MLOps tools is required. Start building scalable and reliable machine learning infrastructure today.