The MLOps Deployment and LifeCycling course offers a comprehensive exploration of the modern MLOps framework, focusing on the lifecycle and deployment of machine learning models. Throughout this course, participants will gain valuable insights into writing ML code that minimizes technical debt, understanding the necessary tools for deploying and monitoring models, and analyzing various types of environments and analytics encountered in the process. The course begins by delving into the MLOps lifecycle, emphasizing the significance of MLOps and the essential functional components for successful model development, deployment, monitoring, and maintenance. Participants will learn about the crucial steps involved in collecting, preparing, and labeling data, conducting experiments on different models, and validating concepts with a champion model. This knowledge will serve as a foundation for the subsequent stages of building, deploying, monitoring, and maintaining models destined for production. By navigating this second chapter of the model's journey to value delivery, participants will establish a benchmark for future endeavors. The course also focuses on developing ML code specifically tailored for deployment. Participants will gain expertise in writing effective ML code, leveraging tools, and training ML pipelines. Additionally, they will explore different deployment environments and determine the appropriate circumstances for their utilization. Strategies for replacing existing production models and an examination of APIs will also be covered. Monitoring and maintaining ML models are crucial aspects of the MLOps lifecycle, and this course provides participants with the necessary knowledge and skills to excel in these areas. Participants will discover the essential performance metrics for monitoring models and ensuring their continued effectiveness. Topics such as drift monitoring in production, model feedback, updates, and governance will be thoroughly explored. By the end of the course, participants will have a comprehensive understanding of how to deploy their own models in production using the MLOps lifecycle. They will be equipped with the necessary skills to develop models for deployment, write effective ML code, leverage tools, train ML pipelines, and monitor and maintain models for optimal performance.