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Deploying TensorFlow Models to AWS, Azure, and the GCP

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Deploying TensorFlow Models to AWS, Azure, and the GCP

Deploying TensorFlow Models to AWS, Azure, and the GCP
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours 11M | 303 MB
Genre: eLearning | Language: English

This course will help the data scientist or engineer with a great ML model, built in TensorFlow, deploy that model to production locally or on the three major cloud platforms; Azure, AWS, or the GCP.

Deploying and hosting your trained TensorFlow model locally or on your cloud platform of choice - Azure, AWS or, the GCP, can be challenging. In this course, Deploying TensorFlow Models to AWS, Azure, and the GCP, you will learn how to take your model to production on the platform of your choice. This course starts off by focusing on how you can save the model parameters of a trained model using the Saved Model interface, a universal interface for TensorFlow models. You will then learn how to scale the locally hosted model by packaging all dependencies in a Docker container. You will then get introduced to the AWS SageMaker service, the fully managed ML service offered by Amazon. Finally, you will get to work on deploying your model on the Google Cloud Platform using the Cloud ML Engine. At the end of the course, you will be familiar with how a production-ready TensorFlow model is set up as well as how to build and train your models end to end on your local machine and on the three major cloud platforms. Software required: TensorFlow, Python.

Deploying TensorFlow Models to AWS, Azure, and the GCP

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  1. 部署TensorFlow模型到AWS, Azure和GCP 本教程将会帮助数据科学家或工程师通过一个出色有Tensorflow开发的ML模型,部署到本地或三个主要的云平台:Azure、AWS或GCP。 本地或在你算选择的云平台上实施和托管你所训练的Tensorflow模型都是一个挑战。在本教程中,你将学习如何将你的模型在你所选择的平台变为真正的产品。本教程从集中于如何使用一个通用的TensorFlow模型界面—Saved Model界面保存模型参数,开始。然后,你将学习如何通过在Docker容器中打包所有的依存关系规划本地托管的模型。接下来,你将接触到AWS SAgeMaker服务,这是由亚马逊提供的完整管理的ML服务。最后,你将开始使用Cloud ML Engine部署你自己的模型在Google Cloud平台。学习完本教程,你将熟悉一个准备用于产品的TensorFlow模型是如何被建立的,以及如何在本地机上以及三个主要的云平台开发和训练你的模型。所需软件:TensorFlow、Python。
    wilde(特殊组-翻译)2周前 (05-07)