This program focuses on the practical implementation and management of Machine Learning Operations (MLOps) to ensure effective and scalable AI deployment. Participants will explore the end-to-end lifecycle of machine learning models, including development, testing, deployment, monitoring, and continuous improvement. The program covers topics such as infrastructure setup, model versioning, deployment strategies, CI/CD pipelines, model monitoring, and governance. Hands-on sessions will guide participants through real-world scenarios, utilizing tools like Kubernetes, Docker, TensorFlow Serving, and cloud platforms, ensuring a comprehensive understanding of how to successfully deploy and maintain AI models in production environments.