MLOps
MLOps (Machine Learning Operations) is a set of practices and tools that aim to streamline and automate the end-to-end machine learning lifecycle, from development and training to deployment, monitoring, and maintenance. It combines principles from DevOps, data engineering, and machine learning to ensure ML models are reliable, scalable, and reproducible in production environments. By standardizing workflows, MLOps helps teams collaborate more effectively and reduces the time-to-market for ML applications.
Developers should learn MLOps when building and deploying machine learning models at scale, as it addresses common challenges like model drift, versioning, and infrastructure management. It is essential for organizations that need to maintain high-performing models in production, such as in finance for fraud detection, e-commerce for recommendation systems, or healthcare for predictive analytics. By adopting MLOps, teams can improve model reliability, reduce operational overhead, and ensure compliance with regulatory standards.