AI Workflows in Production: Deploying Models with Docker and APIs

Taking a machine learning model from a local environment to a reliable production system is a critical skill for modern developers. This course guides you through the entire transition, ensuring your AI workflows are stable, scalable, and ready for real-world users. You will start with the fundamental concepts of machine learning operations (MLOps) before moving on to practical model deployment. Through clear, step-by-step written explanations, you will learn how to wrap your models in modern APIs, containerize them for consistency, and establish basic monitoring to track their performance over time. What you'll learn: - Understand the core phases of the AI production workflow and model lifecycle management - Build lightweight, high-performance APIs to serve model predictions using modern frameworks - Containerize machine learning applications using Docker for seamless deployment across environments - Configure basic MLOps monitoring and observability to track model drift and system health - Apply structured testing practices to validate model endpoints before they go live - Explore cloud-based machine learning tools and Watson workflows for enterprise scaling The course begins with foundational definitions of production environments and model serving, then walks you through designing APIs, packaging them with Docker, and setting up basic observability practices. This course is designed for beginner developers, data enthusiasts, and aspiring ML engineers who want to understand the deployment side of AI, with no prior DevOps experience required. Start reading today to bridge the gap between machine learning theory and production-ready applications.