MLOps System

MLOps (Machine Learning Operations) streamlines the entire lifecycle of machine learning models, from experimentation and training to deployment and monitoring in production environments, aiming to improve efficiency and reliability. Current research emphasizes automating model training and deployment, integrating with existing CI/CD pipelines, and enhancing model monitoring and alerting systems, often leveraging tools like MLflow, Weights & Biases, and containerization technologies. The successful implementation of MLOps practices is crucial for bridging the gap between research and real-world applications of machine learning, enabling faster development cycles and more robust, trustworthy AI systems across various industries.