MLOps (Machine Learning Operations) bridges the gap between machine learning development and production deployment, focusing on building scalable, automated, and reliable ML workflows. This course begins with an overview of the ML lifecycle, identifying the challenges in transitioning from model experimentation to real-world deployment. Learners explore concepts like reproducibility, model versioning, CI/CD for ML, testing ML pipelines, and monitoring performance post-deployment. The course covers tools such as MLflow for experiment tracking, DVC for data versioning, and TFX or Kubeflow for orchestration. Model serving is practiced through TensorFlow Serving, TorchServe, FastAPI, and Docker containerization. Learners deploy models on AWS SageMaker, GCP Vertex AI, or Azure ML and integrate with Kubernetes for scalable serving. Concepts such as feature stores, model registries, and drift detection are introduced to ensure model integrity over time. Real-world practices like automated retraining, A/B testing, and canary releases are covered. Students also implement observability using Prometheus and Grafana and manage infrastructure as code using Terraform or Helm. Security, governance, and regulatory compliance (e.g., GDPR, SOC 2) are discussed. By the end, learners will be capable of managing the full machine learning lifecycle with production-grade workflows—ideal for ML engineers, DevOps teams, and data scientists working in enterprise environments.