Managing & Monitoring Machine Learning Models with AWS SageMaker | AWS ML Engineer Associate

Hello everyone! Welcome to my third AWS Machine Learning presentation. In this video, I focus on an important part of machine learning that often receives less attention—what happens after a model has been trained. Creating a machine learning model is only the beginning. To keep a model accurate and reliable, it needs to be monitored, tested, updated, and managed throughout its entire lifecycle. In this presentation, I explain how AWS SageMaker provides several services that help developers maintain machine learning models after deployment and make better decisions throughout the development process. Some of the topics covered in this presentation include AWS SageMaker Debugger, which helps identify training issues before they become larger problems, Model Registry and Model Versioning for organizing different model versions, SageMaker Model Monitor for detecting changes in production data, and SageMaker Clarify for improving model transparency and understanding why predictions are made. I also discuss how these AWS services work together to support the complete machine learning lifecycle—from monitoring model training to managing deployments and maintaining model performance over time. Working on this presentation helped me understand that building a machine learning model is not the final step. Continuous monitoring, proper model management, and explainable AI are just as important for creating reliable machine learning solutions that can be trusted in real-world applications. I hope this presentation is helpful for students, AWS learners, and anyone interested in machine learning, artificial intelligence, and cloud computing. If you enjoyed this presentation or found it useful, please consider liking the video, sharing it with others, and subscribing for more AWS Machine Learning presentations and learning content. A special thank you to Professor Dr. Victor Govindaswamy for his guidance, encouragement, and support throughout my AWS learning journey. Thank you for watching! Topics Covered ✔ AWS SageMaker Debugger ✔ Debugger Capabilities & Monitoring Features ✔ Model Registry ✔ Model Versioning ✔ SageMaker Model Monitor ✔ SageMaker Clarify ✔ Model Explainability ✔ Data Drift Detection ✔ Machine Learning Lifecycle Management ✔ AWS Machine Learning Best Practices Hashtags #AWS #AmazonWebServices #SageMaker #AmazonSageMaker #MachineLearning #ArtificialIntelligence #AWSMachineLearning #AWSCertified #MLEngineer #MachineLearningEngineer #SageMakerDebugger #ModelRegistry #ModelVersioning #ModelMonitor #SageMakerClarify #ExplainableAI #ResponsibleAI #ModelMonitoring #DataDrift #ModelManagement #CloudComputing #CloudTechnology #DataScience #AI #Python #TechLearning #GraduateStudent #StudentPresentation #ConcordiaUniversityChicago #AWSCloud #AWSCommunity #MachineLearningProjects #CloudLearning #Technology #ComputerScience #LearningJourney #YouTubeEducation #AIEducation #FutureEngineer #AWSML