This document outlines the machine learning lifecycle and architecture including data preparation, model building and training, deployment, and DevOps processes. It shows how raw data moves from data lakes to optimized data lakes and clean data stores. Models are built, tracked, and experimented on before being containerized and deployed to production. ML DevOps uses CI/CD to deploy models to AI, BI, and other platforms. Key technologies include Databricks, Azure ML, and Data Lake Analytics.