Description
In this course, you will learn how to use Amazon Sage Maker to build, train, and deploy machine learning models quickly and easily. We will cover topics such as data pre-processing, model training, model deployment, and more. By the end of this course, you will have a solid understanding of how to use Amazon Sage Maker for your own projects. During this course, we will cover a range of topics, including data pre-processing, model training, model deployment, and more. You will also learn how to use Amazon SageMaker Studio, an integrated development environment that provides a single web-based visual interface to perform all machine learning development steps. Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS) that allows developers and data scientists to build, train, and deploy machine learning models at scale. Amazon SageMaker provides a range of managed containers and estimators for popular machine learning frameworks, such as TensorFlow, PyTorch, Scikit-learn, and more. So you don't have to worry about setting up your own environment, but instead can focus on building and training your models. By the end of this course, you will have a solid understanding of how to use Amazon SageMaker to build, train, and deploy machine learning models for your own projects. So let's get started and embark on this exciting journey together!
