$125 | Duration: 1h 6m | Video: h264, 1920×1080 | Audio: AAC, 48kHz, 2 Ch | 725 MBGenre: eLearning | Language: English | November 30, 2018Use the serverless computing approach to save time and moneyVideo DescriptionOne 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 atStyle and ApproachThis 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 ContentsBEGINNING WITH SERVERLESS AND MOTIVATION FOR SERVERLESS DEEP LEARNINGSTART DEPLOYING WITH AWS LAMBDA FUNCTIONSSTART DEPLOYING TENSORFLOW MODELSWORKING WITH TENSORFLOW ON AWS LAMBDACREATING DEEP LEARNING APICREATING DEEP LEARNING PIPELINECREATING DEEP LEARNING WORKFLOWWhat You Will LearnGain practical experience by working hands-on with serverless infrastructures (AWS Lambda)Export and deploy deep learning models using TensorflowBuild a solid base in AWS and its various functionsCreate a deep learning API using AWS LambdaLook at the AWS API gatewayCreate deep learning processing pipelines using AWS functionsCreate deep learning production pipelines using AWS Lambda and AWS Step Functions