Genre: eLearning | Language: English | November 30, 2018
Use the serverless computing approach to save time and money
Video Description
One of the main problems with deep learning models is finding the right way to deploy them within the company’s IT infrastructure. Serverless architecture changes the rules of the game—instead of thinking about cluster management, scalability, and query processing, it allows us to focus specifically on training the model. This course prepares you to use your own custom-trained models with AWS Lambda to achieve a simplified serverless computing approach without spending much time and money. You will use AWS services to deploy TensorFlow models without spending hours training them. You’ll learn to deploy with serverless infrastructures, create APIs, process pipelines, and more. By the end of the course, you will have implemented a project that demonstrates using AWS Lambda to serve TensorFlow models.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda
Style and Approach
This hands-on course supplies step-by-step instructions on how to work with serverless infrastructures on AWS as well as how to deploy deep learning models accordingly.
Table of Contents
BEGINNING WITH SERVERLESS AND MOTIVATION FOR SERVERLESS DEEP LEARNING
START DEPLOYING WITH AWS LAMBDA FUNCTIONS
START DEPLOYING TENSORFLOW MODELS
WORKING WITH TENSORFLOW ON AWS LAMBDA
CREATING DEEP LEARNING API
CREATING DEEP LEARNING PIPELINE
CREATING DEEP LEARNING WORKFLOW
What You Will Learn
Gain practical experience by working hands-on with serverless infrastructures (AWS Lambda)
Export and deploy deep learning models using Tensorflow
Build a solid base in AWS and its various functions
Create a deep learning API using AWS Lambda
Look at the AWS API gateway
Create deep learning processing pipelines using AWS functions
Create deep learning production pipelines using AWS Lambda and AWS Step Functions
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