Model Training, Optimization, and Tuning with Amazon SageMaker

In this presentation, I explain the process of model training, optimization, and tuning with Amazon SageMaker as part of AWS Machine Learning Associate exam preparation. The video covers how machine learning models learn from data, the role of Amazon SageMaker in the ML lifecycle, and how SageMaker helps build, train, optimize, and deploy machine learning models efficiently. I also discuss SageMaker built-in algorithms such as Linear Learner, XGBoost, LightGBM, and k-Nearest Neighbors, along with common algorithm use cases like customer churn prediction, fraud detection, recommendation systems, and customer segmentation. The presentation also covers key training concepts including epochs, batch size, steps, learning rate, loss function, parameters, hyperparameters, train/test split, cross-validation, overfitting, underfitting, and model ensembling. Additionally, I explain important SageMaker features such as Automatic Model Tuning, SageMaker Experiments, SageMaker Debugger, and SageMaker Model Registry, which help improve model performance, track experiments, debug training jobs, and manage model versions before deployment. #AWSMachineLearning #AWSMLAssociate #AWSCertification #AmazonSageMaker #MachineLearning #ModelTraining #HyperparameterTuning #ModelOptimization #SageMakerExperiments #SageMakerDebugger #ModelRegistry #XGBoost #LightGBM #DataScience #MLOps #CloudComputing