MLOps and DevOps (Development and Operations) are related yet distinct disciplines. DevOps primarily focuses on automating and optimizing software development and deployment processes, encompassing areas like continuous integration, delivery, and infrastructure management. In contrast, MLOps extends these principles to machine learning, emphasizing the entire lifecycle management of machine learning models, including data preparation, model training, versioning, deployment, and ongoing monitoring. MLOps also addresses data-centric challenges and the complexities of managing machine learning-specific tools and frameworks. While both aim to enhance collaboration and automation, MLOps tailors these practices to the unique requirements of machine learning projects, ensuring the reliable deployment and performance of AI models in production environments.

MLOps, or Machine Learning Operations, comprises three essential stages in managing machine learning models throughout their lifecycle. The development phase involves data preparation, model training, and version control. The deployment phase focuses on making trained models available in production environments, encompassing packaging, serving, and monitoring. Finally, the monitoring and management phase involves ongoing model performance tracking, drift detection, retraining, and optimization. These stages are iterative, allowing models to adapt to changing data and business needs. MLOps aims to automate and streamline these processes while ensuring that machine learning models remain accurate, reliable, and aligned with organizational objectives.