Since 2018, enterprise organizations have increasingly adopted machine learning (ML) to gain a competitive edge, but the landscape has evolved with the rapid advancements in artificial intelligence (AI). The current trends in MLOps, which combines machine learning and operations, reflect a shift towards mass adoption, increased competition, and the need for high-speed production of AI features. MLOps is likened to DevOps for ML, introducing structure and transparency in the ML pipeline to enable efficient collaboration between data scientists and engineers. Despite its benefits, MLOps is still maturing, with organizations facing challenges across various stages of the ML pipeline, such as setting realistic business requirements, managing data discrepancies, ensuring efficient experimentation and deployment, and maintaining solution stability through monitoring and retraining. Solutions to these challenges include improving communication between stakeholders, centralizing data storage, automating monitoring processes, and optimizing deployment frameworks.