We would first test our application locally, then, later on, deploy our application to the cloud using one of the major cloud service providers. Therefore Kubernetes allows greater flexibility and is more resource-efficient. The main difference between a service like AWS Elastic Beanstalk and Kubernetes is that the former would scale the entire application stack as one entity whereas the latter allows scaling only the required components of the application stack as required. However, Kubernetes provides far greater flexibility in managing the container instances of our app components and offers a far more efficient method of utilizing the available resources. For many applications, such managed services should be enough. Note that we could solve this problem with docker-compose by choosing a good architecture on our cloud service provider or by using managed services such as AWS Elastic Beanstalk, which provides an easy way of load-balancing and auto-scaling applications. The problem we want to solve with Kubernetes is the problem of load-balancing and auto-scaling in the event of a large number of users visiting our application. This ensures that we face minimal issues in deployment. Docker-compose provides an elegant, platform-independent method of containerizing the individual components of our application and starting them together as one single application. In the previous article, we learned how to create this application from scratch (with Django and React) and we also learned how to put our application into production using docker and docker-compose. This article is the continuation of our previous article where we built a simple machine learning app to predict the species of a sample Iris flower.
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