AI Orchestration: From Local Models to Cloud Deployment

Connecting AI models to real-world applications requires more than just running a basic prompt. Developers need to know how to orchestrate models locally for development and scale them securely to the cloud for production. This text-based course guides you through the foundational concepts of AI orchestration, helping you transition from running open-source models on your own machine to deploying robust, cloud-connected AI workflows that utilize external data sources. What you'll learn: - Understand foundational AI orchestration concepts and model architectures - Configure local environments to run open-source language models efficiently - Apply Retrieval-Augmented Generation (RAG) patterns to connect models to custom data - Integrate vector databases to manage and query semantic embeddings - Deploy AI workflows to cloud environments using containerization basics - Practice basic prompt engineering and model evaluation strategies You will start with core terminology and local setup before moving step-by-step into data retrieval, pipeline construction, and cloud deployment strategies. Through structured written explanations and practical code snippets, you will build a solid understanding of modern AI infrastructure. This course is designed for software developers, system administrators, and technology enthusiasts who are new to AI engineering, requiring no prior experience with machine learning. Begin your journey into AI orchestration today and learn how to bridge the gap between local development and cloud production.