MLOps in 5 Minutes: End-to-End Workflow with MLflow, JupyterHub & ArgoCD

Description

This video provides a concise, high-level overview of a production-ready MLOps workflow. We walk through a complete pipeline using JupyterHub for collaboration, MLflow for experiment tracking and model registry, and ArgoCD for GitOps-based deployment. In this quick guide, you'll learn: - How to set up a collaborative environment with JupyterHub - The role of MLflow in tracking experiments and managing model versions (Champion/Challenger) - How to automate deployments with ArgoCD using GitOps principles - Advanced deployment strategies like hot-reloading and canary rollouts NOTE: This is a condensed overview. A full, deep-dive tutorial covering every step in detail will be released soon! Subscribe so you don't miss it. Tools covered: - JupyterHub (Development) - MLflow (Tracking & Registry) - ArgoCD (GitOps Deployment) - FastAPI (Inference Service) - Streamlit (Client App) - Service Foundry (Infrastructure)