Deploying TensorFlow Models to AWS, Azure, and the GCP

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.
Course info
Rating
(12)
Level
Intermediate
Updated
Apr 30, 2018
Duration
2h 11m
Table of contents
Course Overview
Using TensorFlow Serving
Containerizing TensorFlow Models Using Docker on Microsoft Azure
Deploying TensorFlow Models on Amazon AWS
Deploying TensorFlow Models on the Google Cloud Platform
Description
Course info
Rating
(12)
Level
Intermediate
Updated
Apr 30, 2018
Duration
2h 11m
Description

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.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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