English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 26m | 732 MB
Use the serverless computing approach to save time and money
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.
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.
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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