AI Middle Platform Pipeline orchestrates end-to-end machine learning workflows from data ingestion through edge deployment. Data flows from the Data Layer into the Training Platform, where models are developed and versioned in the Model Repository, then deployed to the Inference Service for real-time predictions. The pipeline routes inference outputs to Edge Devices, enabling distributed AI inference at the network edge. This architecture decouples data preparation, model training, storage, and inference, allowing teams to scale each component independently. Fork this diagram to customize for your MLOps stack, add monitoring layers, or integrate with your preferred ML frameworks and deployment platforms.