Member-only storyVishal RajputFollowTowards Data Science--ShareModel deployment is tricky; with the continuously changing landscape of Cloud platforms and other AI-related libraries updating almost weekly, back compatibility and finding the correct deployment method is a big challenge. In today’s blog post, we will see how to deploy a tflite model on the Google Cloud Platform in a serverless fashion.This blog post is structured in the following way:Let’s first understand what do we mean by serverless because serverless doesn’t mean without a server.An AI model, or any application for that matter can be deployed in several different ways with three major categorisations.Serverless: In this case, the model is stored on the cloud container registry and only runs when a user makes a request. When a request is made, a server instance is automatically launched to fulfill the user request, which shuts down after a while. From starting, configuring, scaling, and shutting down, all of this is taken by the Cloud Run API provided by the Google Cloud platform. We have AWS Lambda and Azure Functions as alternatives in other clouds.Serverless has its own advantages and disadvantages.----Towards Data ScienceAI Researcher/developer| loves writing blogs and codes | 3x🏆Top writer in AI | https://www.linkedin.com/in/vishal-rajput-999164122/Vishal RajputinAIGuys--Miriam SantosinTowards Data Science--19Dominik PolzerinTowards Data Science--22Vishal RajputinAIGuys--2Dominik PolzerinTowards Data Science--22Thomas SmithinThe Generator--118Rahul ParundekarinAI Hero--Orlando MoroniinData Engineer Things--2Mason McGoughinTowards Data Science--Rahul ParundekarinAI Hero--1HelpStatusWritersBlogCareersPrivacyTermsAboutText to speechTeams