Introduction to Docker and deploying scalable ML
Docker is an open-source platform that simplifies the development, deployment, and management of applications. It enables developers to create, package, and distribute applications as lightweight containers that can run consistently across various environments.
Machine Learning (ML) models often require specific dependencies, libraries, and configurations to function properly. Docker simplifies the deployment of ML models by creating an isolated environment that contains all the necessary dependencies.
In this session, we’ll introduce Docker, its main concepts, and how to deploy scalable ML models using Docker containers.
Docker Basics
- a. Dockerfile: A text file containing instructions and commands for building a Docker image. It defines the environment, dependencies, and configurations required for the application.
- b. Images: A Docker image is a lightweight, stand-alone, executable package that includes everything needed to run a piece of software, including the code, runtime, system tools, libraries, and settings. Images are built from a series of layers, each representing a set of instructions defined in a Dockerfile.
- c. Containers: A container is a lightweight, stand-alone, and executable software package that includes everything needed to run a piece of software, including the code, runtime, system tools, libraries, and settings.
Deploying Scalable ML Models
Docker can be combined with orchestration tools like Kubernetes or Docker Swarm to deploy scalable ML models. These tools help manage, scale, and distribute Docker containers across multiple nodes or clusters. This allows you to handle high loads and serve multiple requests concurrently, ensuring a reliable and efficient service.