Transitioning a machine learning model from experimentation to production requires a systematic approach known as MLOps, which integrates DevOps principles into the machine learning lifecycle. This involves creating an MLOps pipeline that automates and manages data, features, and models while ensuring reproducibility and reliability. The pipeline covers various phases, including business and ML solution design, data ingestion and preparation, experimentation, model development, training, validation, packaging, deployment, and monitoring. Each phase involves automating processes such as data versioning, experiment tracking, and model testing to maintain consistency and transparency. Advanced MLOps practices include continuous integration and deployment, robust monitoring, and setting up feedback loops for continuous improvement. The importance of MLOps lies in reducing risks, accelerating innovation, and meeting regulatory requirements through clear model lineage and bias monitoring. By following structured steps, organizations can build scalable and adaptable pipelines that meet evolving data and business needs, ultimately enhancing the delivery of consistent value through machine learning models.