Day 43: MLOps Platform Setup - Building Production ML Infrastructure

Description

Today marks our entry into Phase 3 where we combine everything we've learned about Kubernetes, security, and observability to build a production MLOps platform. We'll deploy a complete ML lifecycle management system featuring experiment tracking with MLflow, model training service with distributed job orchestration, model serving API with versioning, real-time monitoring dashboard with drift detection, and ML pipeline orchestrator for automated workflows. This isn't a toy ML system. We're building the infrastructure that powers ML at companies like Netflix (recommendation engines), Spotify (music personalization), and Uber (demand forecasting). By day's end, you'll have a working platform that can train models, version them, serve predictions, and monitor everything in production.