Serverless Deep Learning with TensorFlow and AWS Lambda [Video]
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-LambdaStyle 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.
|Course Length||1 hour 26 minutes|
|Date Of Publication||30 Nov 2018|
|What Is Serverless?|
|Why Serverless Deep Learning|
|Where Serverless Deep Learning Works and Where It Doesn’t Work|
|Example Projects That We Will Build During the Course|
|Creating an API Gateway Connection to AWS Lambda Using AWS Console|
|Creating an API Gateway Connection to AWS Lambda Using Serverless Framework|
|Example Project – Deep Learning API|
|Creating AWS SQS Connection to AWS Lambda Using AWS Console|
|Creating AWS SQS Connection to AWS Lambda Using Serverless Framework|
|Example Project – Deep Learning Pipeline|