Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate with ML. You will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.Course level: IntermediateDuration: 1 day ActivitiesThis course includes presentations, hands-on labs, and demonstrations. Updated Jan 2026 Module 1: Introduction to Machine LearningBenefits of machine learning (ML)Types of ML approachesFraming the business problemPrediction qualityProcesses, roles, and responsibilities for ML projectsModule 2: Preparing a DatasetData analysis and preparationData preparation toolsDemonstration: Review Amazon SageMaker Studio and NotebooksHands-On Lab: Data Preparation with SageMaker Data WranglerModule 3: Training a ModelSteps to train a modelChoose an algorithmTrain the model in Amazon SageMakerHands-On Lab:
