Automated Machine Learning Deployment and Evaluation

Transitioning a machine learning model from a local notebook to a production environment can be a fragile, manual process prone to errors. This course teaches you how to establish automated pipelines that evaluate, package, and deploy your models safely and consistently. You will transition from manual model handoffs to a modern MLOps workflow. By reading our structured explanations and analyzing practical configuration and code examples, you will understand how to set up continuous integration, run automated evaluation tests, and monitor model performance in production. What you'll learn: - Understand the fundamental phases of the MLOps lifecycle and model deployment strategies. - Automate model testing and validation to ensure performance does not degrade before release. - Configure CI/CD pipelines to package and deploy models automatically. - Apply model monitoring and basic observability techniques to detect drift in production. - Implement version control for both code and model artifacts to ensure reproducibility. We start with core terminology and foundational deployment concepts before moving step-by-step through automated testing, packaging, and pipeline integration. This course is designed for beginner data scientists, software engineers, and aspiring MLOps practitioners. No prior deployment experience is required. Start building robust, automated deployment pipelines today.