Day 04 of MLOps: Deploy and Serve a Machine Learning Model Using Docker and Flask

2026-05-18 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

This guide details the process of deploying and serving a trained machine learning model using Docker and Flask, a critical component of the MLOps lifecycle. It outlines how to expose a model via an API for real-time inference, enabling applications and users to interact with it. The content differentiates between the roles of ML Engineers, who focus on making models usable for inference, and MLOps Engineers, who automate, deploy, scale, monitor, and maintain these systems in production. The practical steps involve creating an API for a trained model, running inference through HTTP requests, and containerizing the entire ML application using Docker for production deployment.

Key takeaway

For MLOps Engineers deploying machine learning models, understanding how to containerize your application with Docker and expose it via a Flask API is fundamental. This approach ensures your models are production-ready, scalable, and easily maintainable. Focus on automating the deployment pipeline to streamline updates and ensure consistent performance across environments.

Key insights

Model deployment and serving are crucial for making trained ML models accessible for real-time inference.

Principles

• MLOps extends ML engineering into production.
• APIs enable real-time model interaction.

Method

Expose a trained ML model via a Flask API, containerize the application with Docker, and serve it for HTTP-based inference requests.

In practice

• Use Flask for simple model APIs.
• Containerize with Docker for portability.

Topics

• MLOps
• Machine Learning Model Deployment
• Docker Containerization
• Flask API
• Model Serving

Best for: MLOps Engineer, Machine Learning Engineer, AI Engineer

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• Docker for Python & Data Projects: A Beginner’s Guide — Docker provides reproducible environments for Python data projects, simplifying dependency management and deployment.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.