MLOps with MLflow and Kubeflow: Track and Orchestrate AI Models

Moving machine learning models from experimental Jupyter notebooks to reliable production environments is one of the biggest challenges in modern AI. This course teaches you how to bridge that gap using industry-standard MLOps tools. You will learn how to design, track, and automate robust machine learning workflows. By understanding the core principles of MLflow and Kubeflow, you will gain the skills to package models, track experiments, and orchestrate complex pipelines on Kubernetes. What you'll learn: Understand the foundational concepts of MLOps, AIOps, and the machine learning lifecycle. Track machine learning experiments, parameters, and metrics using MLflow. Package models systematically for reproducible deployment across different environments. Orchestrate end-to-end machine learning workflows using Kubeflow Pipelines on Kubernetes. Manage model versions and transitions using a centralized model registry. Apply clean, production-grade code structures to transition away from experimental notebooks. The course begins with essential MLOps terminology and foundational concepts before moving into step-by-step written guides on setting up tracking servers, configuring pipelines, and managing model lifecycles. This course is designed for aspiring MLOps engineers, data scientists, and developers looking to transition into production AI. No prior DevOps or Kubernetes experience is required to get started. Start reading today to transform your experimental code into reliable, automated AI pipelines.