AWS Machine Learning with SageMaker: Hands-On

Build, train, and deploy real machine learning models on AWS using SageMaker—through hands-on labs and real-world projects.This course is designed for developers, data engineers, and aspiring ML practitioners who want practical experience building end-to-end machine learning solutions in the cloud.You won’t just learn theory—you’ll actually build and deploy models.What you’ll learnSet up and use AWS SageMaker for ML workflowsPrepare data: handle missing values, mixed data types, and feature engineeringTrain, tune, and evaluate machine learning modelsDeploy models into production and integrate with applicationsUse Hugging Face and DeepSeek LLMs on AWSPerform A/B testing and safely update production modelsBuild recommender systems, time-series models, and anomaly detection solutionsApply model explainability and fairness techniquesSecure your ML workloads on AWSHands-On Learning ExperienceThrough guided labs, you will:Train and deploy your first SageMaker modelWork with built-in algorithms and custom containers (PyTorch, TensorFlow)Optimize models using automated hyperparameter tuningBuild real-world ML pipelines from scratchModern AI & LLMsGo beyond traditional ML:Deploy Hugging Face models on SageMakerWork with DeepSeek LLMsUnderstand how modern AI fits into AWS workflowsProduction-Ready MLLearn how to:Continuously improve modelsRun A/B testsRoll back safely with zero downtimeWho this course is forDevelopers new to machine