DevOps, DataOps, and MLOps for AI Applications

Modern software development requires more than just writing code; it demands a seamless integration of development, data management, and machine learning operations. This course provides a clear path for anyone looking to bridge the gap between building a model and running it in a production environment. You will gain a comprehensive understanding of how to manage the entire lifecycle of an AI application, from initial data ingestion to continuous model monitoring. By focusing on automated workflows and AI-assisted programming, you will learn to build systems that are scalable, reliable, and efficient. What you'll learn: - Understand the foundational concepts of DevOps, DataOps, and MLOps - Apply AI pair programming techniques to streamline development cycles - Build automated data pipelines for consistent model training - Configure continuous integration and delivery (CI/CD) for machine learning - Practice serving models through web frameworks and APIs - Implement basic observability to monitor model performance in production The course begins with essential terminology and the core philosophy behind operational excellence, then moves into the practical application of these workflows. You will read through detailed explanations and study code examples that demonstrate how to connect these different disciplines. This course is designed for beginner data scientists, analysts, and software engineers who want to understand the operational side of AI. No prior experience with DevOps or cloud infrastructure is required. Start mastering the operational workflows that power modern AI today.