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Orchestrating Models: Machine Learning with Docker Compose
- Docker Compose is a tool for defining and running multi-container Docker applications using a YAML file to configure services.
- It simplifies creating and configuring environments where services interact, allowing you to manage multiple containers with a single command.
- Docker Compose is ideal for development and testing, quickly spinning up services like databases, APIs, and web apps locally.
- In contrast, Kubernetes is suited for managing large-scale production applications, providing stability and scalability.
- Docker Compose handles local development efficiently, while Kubernetes ensures application availability and resource management in production.
- A sample ML project with Docker Compose involves a machine learning service and a PostgreSQL database for storing results.
- The project structure includes defining services in a docker-compose.yml file, creating Dockerfiles for services, and configuring dependencies.
- The ML service involves training a model, creating a Flask API for predictions, and storing results in PostgreSQL.
- Running the project with Docker Compose builds images, starts containers, and runs the Flask model on port 5000.
- Using Docker Compose simplifies managing multiple services for machine learning projects, offering scalability and centralized control.
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