MLOps Foundations: Deploying and Scaling Machine Learning Pipelines

Moving a machine learning model from a local notebook to a reliable production environment is one of the biggest challenges in modern software engineering. This course teaches you how to bridge the gap between data science experimentation and robust operational engineering. Through clear, step-by-step written explanations and hands-on configuration exercises, you will develop the skills to build, deploy, and maintain scalable machine learning pipelines. You will transition from writing isolated training scripts to designing resilient systems that automatically test, deploy, and monitor models in real-world environments. What you'll learn: - Understand the core differences between traditional DevOps and the MLOps lifecycle. - Containerize machine learning applications using Docker for consistent environment deployment. - Configure automated CI/CD pipelines to validate and deploy model updates seamlessly. - Monitor production models for performance degradation, data drift, and system health. - Orchestrate scalable ML workloads using Kubernetes and cloud infrastructure. - Apply modern LLMOps concepts to manage and operationalize large language model workflows. The curriculum guides you systematically from local model packaging to cloud-scale orchestration. You will study practical configurations, analyze deployment patterns, and write automation scripts to ensure real-world reliability. This course is designed for aspiring ML engineers, data scientists, and software developers who are new to operational workflows. No prior DevOps experience is required, as we begin with foundational terminology and basic concepts before advancing to deployment architectures. Start building reliable, automated machine learning pipelines today.