Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda
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 book 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 and deploying them. You'll learn to deploy with serverless infrastructures, create APIs, process pipelines, and more with the tips included in this book.
By the end of the book, you will have implemented your own project that demonstrates how to use AWS Lambda effectively so as to serve your TensorFlow models in the best possible way.
|Course Length||3 hours 46 minutes|
|Date Of Publication||31 Jan 2019|
|What is serverless computing?|
|Lambda function – AWS implementation of FaaS|
|Traditional versus Serverless architecture using Lambda|
|Introduction to AWS SQS|
|Creating an AWS SQS connection using an AWS Console|
|Creating an AWS SQS connection using the serverless framework|
|Example project – deep learning pipeline|