Machine Learning Operations (MLOps) is a field discipline that bridges the gap between machine learning model development and production deployment. It focuses on streamlining the process of transitioning machine learning models into production and maintaining them reliably. By adopting appropriate MLOps practices, organisations can accelerate the model development process while ensuring the reliability and performance of models deployed in production. As orginisations increasingly rely on machine learning systems for critical operations, MLOps has become very essential.

This course will teach you the principles and workflows of MLOps, covering practical techniques including data management, model development, Continuous Integration/Continuous Delivery (CI/CD) automation, deployment, and monitoring. You will learn to design and implement end-to-end machine learning pipelines using appropriate tools and technologies. You will also develop the skills to critically analyse and evaluate the performance of deployed machine learning systems and to identify related issues. Finally, you will be able to articulate the security, governance, ethical, and professional considerations that are required for the responsible use of machine learning systems.