Technical requirements
The following items are required for this chapter's learning:
- GitHub repository code for this chapter: https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow/tree/main/chapter08.
- Ray serve and
mlflow-ray-serveplugin: https://github.com/ray-project/mlflow-ray-serve. - AWS SageMaker: You will need to have an AWS account. You can create a free AWS account easily through the free signup website at https://aws.amazon.com/free/.
- AWS command-line interface (CLI): https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html.
- Docker Desktop: https://www.docker.com/products/docker-desktop/.
- Complete the example in Chapter 7, Multi-Step Deep Learning Inference Pipeline, of this book. This will give you a ready-to-deploy inference pipeline to use in this chapter.
