Using TensorFlow with Amazon Sagemaker
6288 ya inscrito
6288 ya inscrito
Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will learn how to train and deploy an image classifier created and trained with the TensorFlow framework within the Amazon Sagemaker ecosystem. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. However, it is possible to use Sagemaker for custom training scripts as well. We will use TensorFlow and Sagemaker's TensorFlow Estimator to create, train and deploy a model that will be able to classify images of dogs and cats from the popular Oxford IIIT Pet Dataset. Since this is a practical, project-based course, we will not dive in the theory behind deep learning based image classification, but will focus purely on training and deploying a model with Sagemaker and TensorFlow. You will also need to have some experience with Amazon Web Services (AWS). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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En un video de pantalla dividida, tu instructor te guía paso a paso
por FC29 de mar. de 2021
Good tutorial for anyone new to AWS Sagemaker and wanting to learn how to deploy a basic TensorFlow model
por JB13 de abr. de 2021
Further study is required here, some cat images were classified as dogs!
por SC26 de sep. de 2020
Great project and awesome customization. I got to learn a lot and practice what I learned in this class. Thanks to Amit for teaching this class.