Key Challenges and Limitations of MLOps in Context of Machine Learning

MLOps (Machine Learning operations) have emerged as an important practice for optimizing model production and deployment. This study investigates the fundamental principles of MLOps, including its historical evolution and differentiation from DevOps. Key components, including continuous integration, continuous deployment, monitoring, logging, and data versioning, are thoroughly covered. The lifecycle of MLOps, which includes data preparation, model training, deployment, and maintenance, is thoroughly examined. Various MLOps tools and platforms, both open-source and commercial, are evaluated. The obstacles of deploying MLOps, such as scalability and data management, are discussed, along with potential solutions. Case studies from healthcare, banking, and retail highlight how MLOps improve operational efficiency and model performance. The study finishes by analyzing future developments in MLOps, such as automation, edge computing integration, and ethical AI practices, emphasizing the revolutionary potential of machine learning productivity and efficiency