AWS SageMaker Lecture | Build, Train & Deploy ML Models at Scale | MLOps |

In this lecture, we explore AWS SageMaker, Amazon’s fully managed service for building, training, and deploying machine learning models at scale. This session is ideal for students, researchers, and professionals working in MLOps, AI, and Cloud Computing. What you will learn in this video: Introduction to AWS SageMaker and its ecosystem Data preparation and feature engineering in SageMaker Training ML models using built-in and custom algorithms Hyperparameter tuning and optimization Model deployment using SageMaker endpoints Monitoring and managing ML models in production Real-world use cases of SageMaker AWS SageMaker simplifies the end-to-end ML lifecycle, making it easier to move from experimentation to production in a scalable and efficient way. Who should watch this? BS/MS Computer Science & Data Science students MLOps & Cloud Engineers AI Researchers and Practitioners Anyone interested in scalable machine learning systems Related Topics: Kubernetes for orchestration Kubeflow for ML pipelines MLFlow for experiment tracking CI/CD using GitHub Actions Monitoring with Prometheus & Grafana If you find this lecture helpful, don’t forget to Like, Share, and Subscribe for more content #AWSSageMaker #MLOps #MachineLearning #AWS #CloudComputing #AI #DataScience