Model Deployment with MLOps focuses on operationalizing machine learning models using automated workflows and scalable infrastructure in production environments. It enables organizations to deploy, manage, and monitor ML models efficiently while ensuring reliability and performance. This training explains core concepts such as model packaging, deployment pipelines, containerization, and inference services. It also covers CI/CD integration, version control, monitoring, rollback strategies, and cloud-native deployment techniques. You will learn how enterprises use MLOps practices to streamline model deployment, reduce downtime, and support continuous delivery of AI solutions. The course also highlights best practices for building secure, scalable, and production-ready machine learning deployment systems.
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