Introduction to Containers as Reproducible Models
Explore the use of containers to create reproducible and isolated environments essential for deploying data science models. Understand how Docker and container orchestration tools like ECS and Kubernetes enable scalable, fault-tolerant, and cloud-portable model deployments. This lesson guides you through containerizing a web endpoint, managing cloud resources, and balancing cost efficiency in production scenarios.
Need for containers
When deploying data science models, it’s important to be able to reproduce the same environment used both for training and serving. In Chapter :
Models as Web Endpoints, we used the same machine
for both environments, and in Chapter 3 : Models as Serverless Functions we used a requirements.txt file to ensure that the serverless ecosystem used for serving the model matched our development environment. Container systems such as Docker provide a tool for building reproducible environments, and they are much lighter weight than alternative approaches such as virtual machines.
Isolated environments
The idea of a container is that it is an isolated environment in which you can set up the dependencies that you need in ...