How to Deploy AI Models in Production Using Docker & Kubernetes
Deploying AI models in production is one of the most important and most challenging steps in the machine learning lifecycle. Building models is only half the job. The real value is realized when those models are deployed, scaled, monitored, and integrated seamlessly with real-world applications. That’s where Docker and Kubernetes become essential. Today, Docker and Kubernetes form the backbone of modern MLOps. They help data scientists and engineers package models, standardize environments, orchestrate workloads, and scale AI applications for millions of users. This comprehensive guide walks you through how to deploy AI models in production using Docker & Kubernetes, with step-by-step explanations, best practices, and real-world engineering insights. For learners exploring the Best Artificial Intelligence Course Online , mastering these deployment skills is essential to becoming industry-ready and understanding how real AI systems operate at scale.1. Why Production Deploy...