This is the code repository for Serverless Deep Learning with TensorFlow and AWS Lambda [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- Explore how machine learning is changing the world we live in.
- Configure UIs with camera settings and use them in your app.
- Implement text recognition and deploy it with Firebase on the cloud.
- Perform face detection by adding it to your app and trying it out!
- Scan through barcodes by adding the barcode scanning feature to your app.
- Identify images by image labeling and deploy them with Firebase on the cloud.
- Add features such as landmark recognition to your apps to identify a specific landmark.
To fully benefit from the coverage included in this course, you will need:
This course will benefit data scientists who want to learn how to deploy models easily, and beginners who want to learn about deploying into the cloud. No prior knowledge of Tensorflow or AWS is required.
This course has the following software requirements:
AWS Lambda subscription