MLOps, or Machine Learning Operations, is a set of practices that combine machine learning (ML), data engineering, and DevOps to streamline and automate the end-to-end ML model lifecycle. MLOps is an essential aspect of the current data science workflows. It is a foundational component of the contemporary information technology landscape, and its influence is expected to increase significantly in the coming years. It encompasses everything from data processing and model development to deployment, monitoring, and continuous improvement, making it a crucial discipline in integrating machine learning into production environments.
However, a significant challenge in MLOps lies in the demand for scalable and flexible infrastructure capable of handling the distinct requirements of machine learning workloads. While the development cycle is often experimental, typically using interactive tools like Jupyter notebooks, production deployment requires automation and scalability.