Serverless Deep Learning with TensorFlow and AWS Lambda [Video]

More Information
  • 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

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

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.

  • Save your time by deploying deep learning models with ease using serverless infrastructures
  • Develop a solid grip on AWS services (AWS Lambda, Simple Query Service, API Gateway, and Step functions) 
  • Start building deep learning APIs, followed by mastering processing pipelines and finally deployment pipelines.
Course Length 1 hour 26 minutes
ISBN 9781789618679
Date Of Publication 30 Nov 2018


Rustem Feyzkhanov

Rustem Feyzkhanov is a machine learning engineer at Instrumental. He works on creating analytical models for the manufacturing industry. He is also passionate about serverless infrastructures and AI deployment. He has ported several packages on AWS Lambda, ranging from TensorFlow/Keras/sklearn for machine learning to PhantomJS/Selenium/WRK for web scraping. One of these apps was featured on the AWS serverless repository's home page.