LOps, or Machine Learning Operations, is a set of practices that combines Machine Learning, DevOps, and Data Engineering to streamline the entire ML lifecycle. This comprehensive guide explores the key components of MLOps, including data management, model development, deployment, monitoring, and governance. Discover how MLOps differs from DevOps and learn about the benefits of adopting MLOps practices in your organization. Whether you’re a data scientist, ML engineer, or part of an operations team, this guide will help you understand how MLOps can enable faster development, improved reliability, and increased efficiency in your Machine Learning initiatives.