Transitioning from proof-of-concept to deployment is a change many machine learning models struggle to make. While most industrial machine learning (ML) projects target the development of highly performant and scalable ML systems in production, it is often difficult to automate and operationalise these systems so that they work perfectly.

In this talk, we will walk you through the two popular architectures - REST and gRPC using Python, docker and TensorFlow Serving which can be used to deploy your machine learning models in production. We will also discuss the pros and cons of the two architectures, solving your dilemma and ensuring a smooth transition of your models from proof-of-concept to production.