Administrative information

This Lecture will provide an overview/foundation for Serving Tensorflow models. The lecture will provide some foundations and background (including some code snippets) that will be required for the following tutorial that will put into practice the MLOps process of Serving a model for production purposes. Specifically the lecture will cover:

• The development of a exemplar model using the Stanford Dogs dataset
• Saving the model and the format of the saved model
  • Tensorflow TFX Serving overview
  • The APIs TFX Serving uses (gRPC and Restful)
• Containerisation
  • Docker containers overview
  • Tensorflow Serving Docker Image
• Serving a model to a local IP address
  • Taking a Docker image and creating and running a Docker container binding the Tensorflow model in transit
• Serving a Model using Azure Container Instances (ACI)
  • Taking a Docker image and creating and running a Docker container, and binding a Tensorflow model.
  • Committing the Docker Container with the model saved as a new Docker Image.
  • Running the new image as a Docker Container in ACI - using the Docker CLI.