fast api
ML Model: Building and Deploying using StreamLit, Docker, GKE
Responsible for replacing and updating pods as and when needed without any downtime. Services: Services are responsible for routing and load-balancing traffic from external and internal sources to pods. Whenever a pod is deployed or replaced, its IP address changes. Hence a stable address provider is needed. A service provides stable IP addressing and DNS name to pods. We will define deployment and service for each of our applications.
Step-by-step Approach to Build Your Machine Learning API Using Fast API
No matter how efficient your Machine Learning model is, it will only be useful when it creates value for the Business. This can not happen when it's stored in a folder on your computer. In this fast-growing environment, speed and good deployment strategies are required to get your AI solution to the market! This article explains how Fast APIcan help on that matter. We will start by having a global overview of Fast API and its illustration by creating an API.
Step-by-step Approach to Build Your Machine Learning API Using Fast API
No matter how efficient your Machine Learning model is, it will only be useful when it creates value for the Business. This can not happen when it's stored in a folder on your computer. In this fast-growing environment, speed and good deployment strategies are required to get your AI solution to the market! This article explains how Fast APIcan help on that matter. We will start by having a global overview of Fast API and its illustration by creating an API.