Using TensorFlow with Amazon Sagemaker

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このガイド付きプロジェクトでは、次のことを行います。

Prepare custom script for Sagemaker.

Train a TensorFlow model using Sagemaker.

Deploy a TensorFlow trained model using Sagemaker.

Clock2 hours
Advanced上級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

Deep Learningimage classificationMachine LearningsagemakerTensorflow

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  1. Download the data

  2. Prepare the dataset

  3. Create the model

  4. Data generators

  5. Arguments

  6. Finalizing the training script

  7. Upload Dataset to S3

  8. TensorFlow Estimator

  9. Deploy the model

  10. Inference and Deleting Endpoint 

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

よくある質問

よくある質問

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。