Machine learning (ML) has seen explosive growth in recent years, leading to increased demand for robust, scalable, and efficient deployment methods. Traditional approaches often need help operationalizing ML models due to factors like discrepancies between training and serving environments or the difficulties in scaling up. This article proposes a technique using Docker, an open-source platform designed to automate application deployment, scaling, and management, as a solution to these challenges. The proposed methodology encapsulates the ML models and their environment into a standardized Docker container unit. Docker containers offer numerous benefits, including consistency across development and production environments, ease of scaling, and simplicity in deployment. The following sections present an in-depth exploration of Docker, its role in ML model deployment, and a practical demonstration of deploying an ML model using Docker, from the creation of a Dockerfile to the scaling of the model with Docker Swarm, all exemplified by relevant code snippets. Furthermore, the integration of Docker in a Continuous Integration/Continuous Deployment (CI/CD) pipeline is presented, culminating with the conclusion and best practices for efficient ML model deployment using Docker.

What Is Docker? 

As a platform, Docker automates software application deployment, scaling, and operation within lightweight, portable containers. The fundamental underpinnings of Docker revolve around the concept of ‘containerization.’ This virtualization approach allows software and its entire runtime environment to be packaged into a standardized unit for software development.